{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 函数图像\r\n",
    "\r\n",
    "画出公式$f(x)=\\sin^6(x-2) \\ e^{-x^3}$在x∈[0,2]式的函数图像，并显示title、xlabel、ylabel。  \r\n",
    "\r\n",
    "示例图：![在这里插入图片描述](https://img-blog.csdnimg.cn/d1de867a05444f4cba3d79a46baa6095.png)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "def f(x):\r\n",
    "    return np.sin(x-2)**6 * np.exp(-x**3)\r\n",
    "t = np.arange(0.0,2.0,0.01)\r\n",
    "\r\n",
    "plt.plot(t,f(t))\r\n",
    "plt.title('f(x)=sin(x-2)^6e^(-x^3)')\r\n",
    "plt.xlabel('x')\r\n",
    "plt.ylabel('f(x)')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 损失率准确率\r\n",
    "\r\n",
    "给出如下两个列表，分别为损失率和准确率，每个列表有101个数据，请根据示例图画出相关图像，并画出一条y=1的趋近线便于对比：\r\n",
    "\r\n",
    "![在这里插入图片描述](https://img-blog.csdnimg.cn/92551d90d9eb433cb39aa5cd1be6add5.png)\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "loss = [3.9697616, 3.7570593, 3.708448, 3.6765559, 3.5213854, 3.1943338, 2.8143737, 2.7303052, 2.3193848, 2.1427655, 1.7266005, 1.5195117, 1.2515366, 1.2058204, 1.1884695, 1.0548707, 0.9672838, 0.7984142, 0.69986844, 0.5801862, 0.59283286, 0.42576936, 0.40954226, 0.4149007, 0.48255992, 0.43888214, 0.4120154, 0.28382322, 0.3709384, 0.27007085, 0.4641089, 0.4064053, 0.3845456, 0.3657792, 0.67280877, 0.4392808, 0.30519733, 0.3105978, 0.27883688, 0.2561565, 0.52563167, 0.35341987, 0.24763991, 0.31171104, 0.12621145, 0.285903, 0.28037775, 0.13856614, 0.23570763, 0.36625412, 0.39813405, 0.14873299, 0.19632441, 0.12007101, 0.18790948, 0.13021031, 0.28469694, 0.15627947, 0.3400484, 0.11596036, 0.15279804, 0.13129614, 0.11490436, 0.14941366, 0.12922022, 0.351205, 0.16458397, 0.24951515, 0.069562204, 0.1922278, 0.18575865, 0.09645326, 0.18188444, 0.20364395, 0.19532162, 0.18028107, 0.09446733, 0.13204116, 0.26251015, 0.12308956, 0.117592394, 0.2106612, 0.1770253, 0.22549969, 0.12268591, 0.17754334, 0.09156803, 0.099512674, 0.09060512, 0.0684826, 0.12218574, 0.10688785, 0.054800004, 0.14710936, 0.09565897, 0.29208595, 0.1422411, 0.10551404, 0.043301877, 0.15537971, 0.120202035]\r\n",
    "accuracy = [0.1, 0.1, 0.06, 0.06, 0.15, 0.06, 0.25, 0.34, 0.36, 0.53, 0.64, 0.67, 0.64, 0.76, 0.74, 0.72, 0.84, 0.9, 0.84, 0.79, 0.88, 0.86, 0.94, 0.93, 0.95, 0.88, 0.88, 0.93, 0.95, 0.96, 0.96, 0.92, 0.92, 0.92, 0.96, 0.96, 0.94, 0.94, 0.96, 0.91, 0.96, 0.95, 0.94, 0.98, 0.97, 0.96, 0.96, 0.94, 0.92, 0.97, 0.97, 0.95, 0.98, 0.98, 0.98, 0.96, 0.98, 0.94, 0.96, 0.98, 0.97, 0.98, 0.99, 0.95, 1.0, 0.95, 0.98, 0.98, 0.96, 0.98, 0.98, 0.96, 0.97, 0.97, 0.98, 0.96, 0.95, 0.98, 0.96, 0.99, 0.99, 0.97, 1.0, 0.97, 0.98, 0.95, 0.99, 0.99, 0.97, 0.97, 0.98, 0.98, 0.97, 0.98, 0.98, 0.97, 0.98, 1.0, 0.97, 0.99, 0.98]\r\n",
    "x=range(0,1010,10)\r\n",
    "y=[1 for _ in range(101)]\r\n",
    "\r\n",
    "plt.plot(x,loss,linestyle='-',color='black',label='loss')\r\n",
    "plt.plot(x,accuracy,linestyle='-',color='red',label='accuracy')\r\n",
    "plt.plot(x,y,linestyle='--',color='blue')\r\n",
    "plt.legend()\r\n",
    "\r\n",
    "plt.title('loss / accuracy')\r\n",
    "plt.xlabel('loss')\r\n",
    "plt.ylabel('step')\r\n",
    "\r\n",
    "plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 间断点的探讨\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "如示例图所示，参考高数内容，讨论并画出四种不同的间断点图像。\r\n",
    "```py\r\n",
    "#可去间断点：函数在该点左极限、右极限存在且相等，\r\n",
    "#但不等于该点函数值或函数在该点无定义。\r\n",
    "#如函数y=（x^2-1)/(x-1)在点x=1处。(图一)\r\n",
    "y1=(x**2-1)/(x-1);\r\n",
    "#跳跃间断点：函数在该点左极限、右极限存在，但不相等。\r\n",
    "#如函数y=|x|/x在点x=0处。（图二）\r\n",
    "y2=np.abs(x);\r\n",
    "#无穷间断点：函数在该点可以无定义，且左极限、右极限至少有一个不存在，且函数在该点极限为∞。\r\n",
    "#如函数y=tanx在点x=π/2处。（图三）\r\n",
    "y3=np.tan(x)\r\n",
    "#振荡间断点：函数在该点可以无定义，当自变量趋于该点时，函数值在两个常数间变动无限多次。\r\n",
    "#如函数y=sin(1/x)在x=0处。（图四）\r\n",
    "y4=np.sin(1/x)    \r\n",
    "```\r\n",
    "示例图：\r\n",
    "\r\n",
    "![在这里插入图片描述](https://img-blog.csdnimg.cn/6d5ebaf19dbf4128b58d2f2f7d99ca4c.png)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "from math import atan, pi\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "\r\n",
    "plt.subplot(221)\r\n",
    "ax = plt.gca()\r\n",
    "ax.xaxis.set_ticks_position('bottom')\r\n",
    "ax.spines['bottom'].set_position(('data', -1))\r\n",
    "ax.yaxis.set_ticks_position('left')\r\n",
    "ax.spines['left'].set_position(('data', -2))\r\n",
    "\r\n",
    "x = np.linspace(-2, 1, 1000)\r\n",
    "def f(x): return (x**2-1)/(x-1)\r\n",
    "\r\n",
    "\r\n",
    "plt.plot(x, f(x), label='removable discontinuity')\r\n",
    "# plt.plot(1, 2-0.15, marker='^', color='red', linewidth=2, markersize=12)\r\n",
    "# plt.text(1,2,'point')\r\n",
    "plt.annotate(\"point\", (1, 2.0), arrowprops=dict(facecolor='red', shrink=0.05))\r\n",
    "plt.xticks(range(-2, 3, 1))\r\n",
    "plt.yticks(np.arange(-1, 2.5, 0.5))\r\n",
    "plt.xlim(-2, 2)\r\n",
    "plt.ylim(-1-0.15, 2)\r\n",
    "plt.legend(loc='center')\r\n",
    "\r\n",
    "\r\n",
    "plt.subplot(222)\r\n",
    "\r\n",
    "# ax = plt.gca()\r\n",
    "# ax.xaxis.set_ticks_position('bottom')\r\n",
    "# ax.spines['bottom'].set_position(('data', 0))\r\n",
    "# ax.yaxis.set_ticks_position('left')\r\n",
    "# ax.spines['left'].set_position(('data', -2))\r\n",
    "\r\n",
    "x = np.linspace(-2, 2, 1000)\r\n",
    "plt.plot(x, np.abs(x), label='jump discontinuity')\r\n",
    "plt.xticks(range(-2, 3, 1))\r\n",
    "plt.yticks(np.arange(0, 2.5, 0.5))\r\n",
    "plt.xlim(-2, 2)\r\n",
    "plt.ylim(0-0.15, 2)\r\n",
    "plt.legend(loc='upper center')\r\n",
    "# plt.plot(0, 0-0.1, marker='^', color='red', linewidth=2, markersize=12)\r\n",
    "# plt.text(0,0,'point')\r\n",
    "plt.annotate(\"point\", (0, 0), arrowprops=dict(facecolor='red', shrink=0.05))\r\n",
    "plt.subplot(223)\r\n",
    "\r\n",
    "ax = plt.gca()\r\n",
    "ax.xaxis.set_ticks_position('bottom')\r\n",
    "ax.spines['bottom'].set_position(('data', -1.0))\r\n",
    "ax.yaxis.set_ticks_position('left')\r\n",
    "ax.spines['left'].set_position(('data', -20))\r\n",
    "\r\n",
    "x = [atan(-1)-pi*7, -pi*7+atan(1.1)]\r\n",
    "while x[-1] < 20:\r\n",
    "    x.append(x[-2]+pi/2)\r\n",
    "plt.plot(x, np.tan(x), label='infinite discontinuity')\r\n",
    "\r\n",
    "plt.xlim(-20, 20)\r\n",
    "plt.ylim(-1, 1)\r\n",
    "plt.yticks(np.linspace(-1.0, 1.0, 5))\r\n",
    "plt.legend(loc='upper center')\r\n",
    "\r\n",
    "\r\n",
    "plt.subplot(224)\r\n",
    "\r\n",
    "\r\n",
    "ax = plt.gca()\r\n",
    "ax.xaxis.set_ticks_position('bottom')\r\n",
    "ax.spines['bottom'].set_position(('data', -1.0))\r\n",
    "ax.yaxis.set_ticks_position('left')\r\n",
    "ax.spines['left'].set_position(('data', -0.5))\r\n",
    "\r\n",
    "x = np.linspace(-0.5, 0.5, 1000)\r\n",
    "plt.plot(x, np.sin(1/x), label='discontinuous point of oscillation')\r\n",
    "plt.xlim(-0.5, 0.5)\r\n",
    "plt.xticks(np.linspace(-0.4, 0.4, 5))\r\n",
    "plt.legend(loc='center')\r\n",
    "\r\n",
    "\r\n",
    "plt.show()\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jiang\\AppData\\Local\\Temp/ipykernel_2200/2887105.py:14: RuntimeWarning: invalid value encountered in true_divide\n",
      "  def f(x): return (x**2-1)/(x-1)\n"
     ]
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     "output_type": "display_data",
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e58cff5TXXHNNCDQ6eygrK5NdunSRhYWF0mazySN5ZXLb0QJZUFbllNm1a5fq8cL1vZZSNoyHIoRorZwtLoQYhN0zymuIuTV8U2m28qcPtvL8d3v5Q9+2fHjXUFolxYRbLQ0v6ISgU/M4jHpBdl45JkvD7aQ/ceIEQ4cOdVY8aYSOFStW0KNHD+6//36aNGnCmVITBeUmWibF1Ng43FgIVdnwEmAkkCyEOAY8ARgBpJRvAhOAu4UQFqACuMlhSTXCxKmiSqa/m8lvx4uYeWV37hl5XqOJ0zYUkVYOb9Dr6NwingM5pRzOK+O8lIQGyXG1bduWffv2hWSskSNHMnLkyJCMdTZw2WWXceTIEcDejeJUUQVNYo20SozMjYuBCFWV180BHn8VeDUUc2nUna1HC5m+KJOyKgtzb8/g8l6twq1SpLIA+/fW3xabtVLKMX4eDwnSUb0UY9TTsXkc2XllHC0op2PzxpOw1fBNpdnK0bxyoo162jer+Zk2pmvvRpTt0QgFy7Ye58b//UKUQcen91yoGRM/SCnXAGHfqh4TE0NeXp5zYUmKNdK6SQxFFWZySqrCrJ1GXbFYbWTnlSGEoHOLePS6msYkLy+PmJjGEYrWenmdI9hskue/38sbqw8yOLU5b9w2kObxjS9GG4EMFUJsA04AD0spd3oTEkJMB6YDdOwYXH+u9u3bc+zYMdzL6EvKTJw+YqVFfBSxUfpaKa8RXqSU5JWaqLLaSEmI5mCR5zW+cmJjY0AzKOcApVUWHvxgCyt253DzoI48ObZ3RHYqbYQo5fClQojR2Mvhu3oTlFLOBeYCZGRkBBXDMBqNNU7rU6g0W7l53gb2nMzhk7uH0rttk2D11wgzjy/bwaJfjvPCDelc1qdxGA1/aKvKWc6RvHKue/1nftyby5Nje/PM+DTNmIQIKWWxlLLUcftrwCiE8DzDt56IMer53+0DaRpnZNrCTHK18Fej4t0Nh1n0y2HuGt6FCQMbvzEBzaCc1Wz4PY9xr63jdHEVi+4cxOQLtTPJQ0kklMO3TIxh3qQM8stN3PVuJlUWa0NOr1FL1h84w+zlO7m0R0v+clWPcKsTMjSDcpay+NfD3Db/V5rHR/H5vcMYdn6DXTifNTjK4X8Bugshjgkh/iiEmCGEULZZTwB2OHIoLxOmcvi0dk349w392HykkL9+9lujqgo6F8k+U8bdizfTJTme/97Ur0YSvrGj5VDOMsxWG//4cheLfjnMyO4pvHxzf5JijOFWq1HSmMrhr+nbhv05XXlpxX56tE5k+vDI70x7LlJcaWbqokyEgPmTM0g8y36bIfFQhBBvCyFyhBA7fDwuhBAvCyEOCCG2CyE8m9xo1JnCchOT397Iol8OM314F96afIFmTM4hHri0K9f0acOz3+xh1Z7T4VZHww2rTfLAki1knynj9VsH0KlFfLhVCjkNdR7K1dirX7piL518I0Tzajg4kFPCuNd+JjO7gBduSOdvo3ueVa60RmB0OsELN6TTu20SDyzZyr7TwXXL1ahf5nyzm9V7c3lyXG8uPO/sDEGHxKCo2AA2Dljk6F22AWgqhGgTirk14Mc9OVz72nrKqqwsmT7krKkY0Qie2Cg98yZlEBulZ+rCTArKwtedWKOajzOPMm/tISYP7eTzGO2zgYZKyrcDjrr8fcxxnwfaeSjqkVIyd81B7ly4iU4t4lh+3zAGdmoWbrU0wkybJrHMvX0gp4oruXtxFmZrwzWS1PAkMzufR5fu4KLzk3lsTK9wq1OvNJRB8RZ78VqKIrXzUFRRabbyfx9v45mv9zA6rQ0fzxhK26ax4VZLI0Lo37EZ/7q+Dxt+z+eJ5Tu1yq8wcaygnBnvZdGuWSyv3TIAQ2M626QWNFSV1zGgg8vf7bG3qtCoBTklldz1bhZbjhTy58u68cCo87X9JRoejO/fnr2nSnnzp4P0aJ3IpKGdw63SOUVZlYVpi7Kostj4YFIGTeLO/gKZhjIoy4H7hBAfAIOBIinlyQaa+6xix/Eipi3KpLDczBu3DuDqPloqSsM3M6/szoGcEp78YhddkhO4qOvZmQyONGw2yUMfbWXvqWLeuWMQ57dMCLdKDUKoyoYDbQD7GvgdOADMA+4JxbznGl9uP8GEN9cjgE/uHqoZE42A6HWCl27qz/kpCdyzOItDZ8rCrdI5wYsr9vHdztM8ek0vRnQ7d0L3DXUeigTuDcVc5yI2m+SlFft4edUBBnZqxpu3DSSlkR7Ao9HwJEQbmD85g7GvruOPCzex9J5hNIk9+8Mv4WLZ1uO8suoAEzM6cOewzuFWp0E5uzNEZwHlJgv3LN7My6sOcMPA9rw/bbBmTDSCpkPzON68bSBH8sq5f8kWLFrlV72w7Wghf/lkO4M6N+cf16adc7lNzaBEMMcKyrn+jV/4ftcp/n5NT56b0Jdog3buhUbtGNylBf+8No01+3J59ps94VbnrON0sf1Y7eSEaN64bcA52dVb6+UVoWRm53PXu1mYrDbennIBI7u3DLdKGmcBNw3qyN7TJby17hDdWiUw8YLgDvvS8E6l2cr0RZmUVlr49J4LaZFwbkYRRKTVp2dlZbU0GAzzgbS8vLzUNm3OvcRzWZWFwgozBp2geXwUxrO8dj1cHDt2zNSyZcvvLBbL1IEDB+Y01LwZGRkyMzOzoabzwGK1cceCTWz4PY/FU4cwKLV52HQ5G5BS8qcPtvLF9hPMvT0j7MdqCyGypJQZ4Zg74jwUg8Ewv3Xr1j1TUlIK9uzZk9qzZ89wq9RgSCk5WVSJubSKLtEGOjaPO+s3QoUTi8ViadmyZa9Tp07NB8aGW5+GwqDX8erNAxj/+s/MeC+LZfcOo0PzuHCr1Wh5ffVBlm87wcwru4fdmISbSFyt0lJSUop1Ol1kuU71jMVmIzuvnDOlVSQnRJOaHK8Zk3pGCEFKSkoRkObj8bO2i3aTOCPzJ2dgsdqYtiiT0ipLuFVqlHy74xTPf7eXcf3acs9I7ciAUO1DuUoIsdfxw5rl5fGRQogiIcRWx7/H/el0rhmTKrOVgzlllFZZaN8slrZNY8+56pBw4fiu+fodLOAs7qLdJSWB124dwP6cUh78YCs22zn1s6szu04U89BHW0nv0JR/Xd9X+80SAoMihNADr2H/cfUCbhZCeOuAtlZK2c/x76m6znu2UFJp5kBuKVabpEtyPM3jz81kXiRyLnTRvrhrCo9d05MVu0/zwvd7w61Oo+FMaRXTFmWSFGNk3u0DiTFq1ZcQGg9lEHBASvm7lNIEfID9h6bhByklZ0qqyD5ThlGv4/yW8cRH+05pzZ49mxdeeMHj/uzsbNLSvEZsVOE67uOPP86KFStqPZZaFixYwIkT1a3cpk6dyq5du2o11okTJ5gwYQIAW7du5euvvw6Jjio5K7poT76wMzcP6sjrqw/y+Zbj4VYn4qmyWJnxbhZ5ZVXMm5RBy6SYcKsUMYQiKe/tRzXYi9xQx9nbJ4CHpZQ7vQ1WVlaWuGPHjhQAmy28m6+klEgp0elCm8uwScmJggryy00kxRjp0DwuIg7DeuqphnEcFyxYQFpaGm3btgVg/vz5tR6rbdu2fPLJJ4DdoGRmZjJ69OiQ6KmCoLpoA3PBXuVVn0oFixCCJ8f25vfcUv7y6XY6J8fTr0PTcKsVkUgp+fvSHWQeLuDVW/rTp32TcKsUUYRipVTzo9oMdJJSpgOvAJ/7Giw+Pr4kLS1td1pa2u63thQx8X+/hPTfk194tWNOsrOz6dmzJ/fccw8DBgzg6NGjPP/881xwwQX07duXJ554winXo0cPpk6dSlpaGrfeeisrVqxg2LBhdO3alY0bNwKQn5/PtddeS9++fRkyZAhZW7Zy8HQJg9N7EG2tpFMLuzE5//zzOX36NF988QWDBw+mf//+XHbZZZw+XX2U67Zt27j00kvp2rUr8+bN89DdarUyc+ZMp67/+9//vL7Gp59+mu7du3PZZZexd291mGPKlCnOxXnWrFn06tWLvn378vDDDwNw+vRpxo8fT3p6Ounp6axfvx6A//znP6SlpZGWlsZLL71U432cNm0avXv35oorrqCiooJPPvmEzMxMbr31Vvr160dFRQUjR45EKaNNSEjg0UcfJT09nSFDhjhfv6tuipwyT1paGiaTiccff5wPP/yQfv368eGHH9K1a1cUb8Bms3H++edz5swZv59/kJw1XbSjDDreuG0grZKimbYok5NFFeFWKSJ5a90hPs46xgOjujKmb9twqxNxhMKgBPxRSSmLpZSljttfA0YhRMS2Pd27dy+TJk1iy5Yt7N27l/3797Nx40a2bt1KVlYWa9asAeDAgQP86U9/Yvv27ezZs4f333+fdevW8cILL/DMM88A8MQTT9C/f3+2b9/OE0/+g9tum0SVVfKHP4xl/apvEELw66+/0rlzZ1q1asVFF13Ehg0b2LJlCzfddBPPPfecU6/t27fz1Vdf8csvv/DUU0/VCBsBvPXWWzRp0oRNmzaxadMm5s2bx6FDh2rIZGVl8cEHH7BlyxY+++wzNm3a5PH68/PzWbp0KTt37mT79u38/e9/B+CBBx5gxIgRbNu2jc2bN9O7d2+ysrJ45513+PXXX9mwYQPz5s1jy5YtAOzfv597772XnTt30rRpUz799FMmTJhARkYGixcvZuvWrcTG1jzDpaysjCFDhrBt2zaGDx/u1XB6IyoqiqeeeoqJEyeydetWJk6cyG233cbixYsBWLFiBenp6SQnh/RrtxyY5Kj2GkIj76LdPD6KtyZfQHmVhemLsqgwWcOtUkTx494cnvl6N1entebBUV3DrU5EEoqQ1yagqxAiFTgO3ATc4ioghGgNnJZSSiHEIOyGLC/QwHdd0IJevRr+hLNOnToxZMgQAL7//nu+//57+vfvD0BpaSn79++nY8eOpKam0qdPHwB69+7NqFGjEELQp08fsrOzAVi3bh2ffvopRRVmOqQNorAgnxZRFibddgtPPfUUd9xxBx988AETJ04E4NixY0ycOJGTJ09iMplITU116jVu3DhiY2OJjY3lkksuYePGjfTr18/5+Pfff8/27dudV/JFRUXs37+/xhhr165l/PjxxMXZ9x2MHeu5/SIpKYmYmBimTp3KNddcw5gxYwBYtWoVixYtAkCv19OkSRPWrVvH+PHjiY+PB+C6665j7dq1jB07ltTUVKd+AwcOdL4n/oiKinLON3DgQH744YeAz/HFnXfeybhx43jwwQd5++23ueOOO4J6vqOL9kggWQhxDHgCMAJIKd/E3kV7NPYu2uVAcBNEIN1aJfLfm/oz7d1MHv5kG6/e3F+rXgIO5JTwwPtb6NE6iX/fmI4uAkLUkUidDYqU0iKEuA/4DtADb0spdyqt6x0/vAnA3UIIC1AB3CQjbYu+C8riCPaY6V//+lfuuuuuGjLZ2dlER1dXZOl0OuffOp0Oi8XifP6ZkipKo8qIizJg0Avio40MHTqUAwcOkJuby+eff+70Au6//34eeughxo4dy+rVq5k9e7ZzDvcftvvfUkpeeeUVrrzySr+vL9ACYTAY2LhxIytXruSDDz7g1VdfZdWqVV5l/X2Mru+PXq+noiJwGMVoNDr10+v1zvfRYDA4c2pSSkymwGeld+jQgVatWrFq1Sp+/fVXp7eilnO1i/ZlvVrxyFU9mPPNHrq3SuSBc/xqvKDMxB8XZhJt1DFvcgZxURG3HzxiCEm2WUr5tZSym5TyPCnl04773nQYE6SUr0ope0sp06WUQ6SU60Mxb0Nw5ZVX8vbbb1NaWgrA8ePHyclR16XDZpP0HzSUhe++R7O4KI7s2ERycjJJSUkIIRg/fjwPPfQQPXv2pEWLFoDdq2jXzl4otHDhwhrjLVu2jMrKSvLy8li9ejUXXHCBh65vvPEGZrMZgH379lFWVvP8i+HDh7N06VIqKiooKSnhiy++8NC7tLSUoqIiRo8ezUsvvcTWrVsBGDVqFG+8Yd9qYbVaKS4uZvjw4Xz++eeUl5dTVlbG0qVLufjii/2+L4mJiZSUlKh6DxU6d+5MVlaW831QXmOgcadOncptt93GjTfeiF6vlXaq5a7hXbiufzv+88M+vvmt0Ubx6ozZauPe9zdzsrCS/90+kHbaMdt+0bZiB+CKK67glltuYejQofTp04cJEyaoWgwlcDC3lDvv/wsHd2/nmpFD+Nvf/lrDSEycOJH33nvPGe4CexnvDTfcwMUXX+wR7x80aBDXXHMNQ4YM4bHHHnNWSSlMnTqVXr16MWDAANLS0rjrrrucV/gKAwYMYOLEifTr14/rr7/e6+JfUlLCmDFj6Nu3LyNGjODFF18E4L///S8//vgjffr0YeDAgezcuZMBAwYwZcoUBg0axODBg5k6daozPOiLKVOmMGPGDGdSXg3Tpk3jp59+YtCgQfz66681vEiFSy65hF27djmT8mAP6ZWWlgYd7jrXEULwzHV96N+xKQ99tI0dx4vCrVJYeOqLXaw/mMcz1/VhYCet51kgIq455LZt27LT09PPAOzatWtgOHIodaWsysLh/HJsNknH5nEkaYcZhY3MzEz+/Oc/s3btWo/HduzYUZ6WlrZ727Ztyenp6Z0bSqdwN4cMhpySSsa9+jMCWHbfRefUWTzvbjjMY5/v4K7hXfjr6MbTUzCczSE1DyXEFJSZ+P1MGToB57dM0IxJGJkzZw7XX389zz77bLhVabS0TIxh3qQMCsrN3PVuJlWWc6Pya/2BM8xevpNLe7TkL1f1CLc6jQbNoIQIe6fgCo4WlBMXpef8lAStHUOYmTVrFocPH+aiiy4KtyqNmrR2Tfj3jelsPlLIXz/7zW8hxtlA9pky7l68mS7J8fz3pn4Rsem4sRCJBsVms9ka1Sdotdk4nFdObkkVLeKjtE7BjQjHd007DzcAo/u04cHLuvLZ5uPMW/t7uNWpN4orzUxdlIkQ8NbkC0iM0SIMwRCJq96O3NzcJo3FqFSZrRzIKaOk0kK7prG0axaHTqvbbxRIKcnNzW0CeG1Pr1GTBy7tyjV92vDsN3tYted04Cc0Mqw2yQNLtpB9pozXbx1AxxbaGTHBEnEF1RaLZeqpU6fmnzp1Ki0vLy+iN1VVma3kl9n3QzSPjyKnRE+DHfunUWdOnTplsNlsuywWy9Rw69IY0OkEL9yQzuH8Mh5YspXP7rmQbq0Sw61WyJjzzW5W783l6fFpXHhexDbyiGhCUuUlhLgK+C/2jY3zpZRz3B4XjsdHY99RPEVKuTnQuJFcDfPuL9nM/mIXXZLjmT85g04tPMtYNSKbcFXDRPL3Wg0niyoY++rPxBr1fH7vMJrHR4VbpTrzUeZR/vLJdiYP7cST42rfvTsSaNRVXirPQ2nUBxG5YrbaeHTpbzy2bCcju6Xw2T0XasZE45yiTZNY5t4+kFPFldyzOAuztXGnoDKz83l06W9cdH4yj41pfNsUIomGOg+l0R9EBPaS4Nvf+pXFvx7hrhFdmDspQ0vaaZyT9O/YjH9d34cNv+fzxPKdjbby61hBOTPey6J9szheu2WAVkxTRxrqPBRfBxF59HQQQkzH7sXQsWPHEKgXGvadLuGPCzdxuriKFyemM75/+3CrpKERVsb3b8++06W8sfogPVonMmlo53CrFBRlVRamLcqiymLjg0kZNInTLg7rSkOdhxLUQURSygwpZUZKSkqdlQsFK3adZvxrP1NptvHh9CGaMdHQcDDziu5c1rMlT36xi3X7Q3rWTL1is0ke+mgre08V8+otAzi/ZUK4VToraJDzUFTKRBxSSt5YfZBp72bSJSWB5fcNo3/HZuFWS0MjYtDpBC/d1J/zUxK4Z3EWh86UBX5SBPDiin18t/M0j17TixHdIuPC9WwgFAbFeR6KECIK+3koy91kGt1BRJVmKw99tI1/fbuHa/q04aO7htKmidZp9FxDCHGVEGKvEOKAEGKWl8dHCiGKhBBbHf8eD4ee4SQh2sD8yRnodYI/LtxEUYVnJ+hIYtnW47yy6gATMzpw57DO4VbnrKLOBkVKaQGU81B2Ax8p56EoZ6JgP4jod+wHEc0D7qnrvPVJTnElE+duYOmW4zx8RTdeubk/sVFaG5VzDZUVjABrpZT9HP+ealAlI4QOzeN487aBHMkr5/4lW7BEaOXXtqOF/OWT7Qzq3Jx/XJsW0fvcGiMh2djoONb3a7f73nS53WgOItp+rJBpizIpqbTw5m0DuSqtdbhV0ggfzgpGACGEUsG4K6xaRSiDu7Tgn9emMeuz33jm6z08/ofIKsE9VVTJtEWZJCdE88ZtA4gyaBVdoUZ7R11Yvu0EN7z5Cwadjk/vvlAzJhq+qhPdGSqE2CaE+EYI0dvbQEKI6UKITCFEZm5ubn3oGhHcNKgjdwzrzNs/H+LDTUfCrY6TSrOV6e9mUlZl4a0pGbRIOHfa8DckmkHBXvHxwnd7eWDJFtLbN2XZfcPo2SYp3GpphB811YmbgU5SynTgFeBzbwNFYvViffHo6J5c3DWZv3++g42H8sOtDlJK/vLJdn47XsRLN/WnR2vtt11fnPMGpazKwoz3snj1xwPcdEEH3ps6mGTt6kXDTsDqRCllsZSy1HH7a8AohDinG0EZ9DpevWUAHZrFMeO9LI7ml4dVn9dXH2T5thPMvLI7l/dqFVZdznbOaYNyNL+c699Yz4rdp3niD7149ro+WlxVw5WAFYxCiNaOXnUIIQZh/03lNbimEUaTWCPzJ2dgsdqYtiiT0ipL4CfVA9/uOMXz3+3l2n5tuXvEeWHR4VzinF09f/09j3Gv/cyJwgoW3jmIO4alahUfGjVQWcE4AdghhNgGvAzcJBtrH5IQ0yUlgdduHcD+nFIe/GArNlvDvi27ThTz0EdbSe/QlDnX99V+3w1AxLWvbwiWbDzCY5/voGPzOOZPzqBLirZLVsM7KioYXwVebWi9GgsXd03hsWt6MvuLXbzw/d4GO073TGkV0xZlkhRjZN7tA7XTUxuIc8qgWKw2/vnVbhasz2Z4txReubk/TbQz3zU06pXJF3Zm7+lSXl99kG6tErm2v7dCudBRZbEy490s8sqq+PiuC2mZFFOv82lUUyeDIoRoDnwIdAaygRullAVe5LKBEsAKWMLRq7+o3Mx9Szazdv8Z/nhRKn+9uofWWVRDowEQQvDk2N78nlvKXz7dTqcWcfXWwkhKyd+X7iDzcAGv3tKfPu2b1Ms8Gt6p64o6C1gppewKrHT87YtLHDuJG9yYHMgp5drXf2bD73k8d31fHhvTSzMmGhoNSJRBxxu3DaRVUjTT383iZFFFvczz1rpDfJx1jAdGdWVM37b1MoeGb+q6qo4DFjpuLwSureN4IWf13hzGv/4zJZVmlkwbwo0XdAj8JA0NjZDTPD6KtyZfQHmVhemLsqgwWUM6/o97c3jm691cndaaB0d1DenYGuqoq0FppTR5dPzf0oecBL4XQmQ5zjvxSah2FEspmb/2d+5csIn2zeL4/N5hZHRuXuvxNDQ06k63Vom8fHN/dpwo4uFPtoXsYK4DOSU88P4WerRO4t83pqPTaRVd4SBgDkUIsQLw1oPk0SDmGSalPCGEaAn8IITYI6Vc401QSjkXmAv2s7eDmMNJlcXKo0t38EnWMa7q3Zp/35hOfPQ5VX+goRGxjOrZikeu6sGcb/bQvVUiD9TRmygoM/HHhZlEG3XMm5xBXJT2Ww8XAd95KeVlvh4TQpwWQrSRUp50HOmb42OME47/c4QQS7E33fNqUOpKbkkVM97LIutwAQ+M6sqDo7pqVysaGhHGXcO7sO9UCf/5YR9dWyZwdZ/anQhuttq49/3NnCysZMn0wbRrqh0xEU7qGvJaDkx23J4MLHMXEELECyESldvAFcCOOs7rlR3Hixj36jp2nijitVsG8NDl3TRjoqERgQgheOa6PvTv2JSHPtrGjuNFtRrnqS92sf5gHs9c14eBnbSQdripq0GZA1wuhNgPXO74GyFEWyGEshmsFbDOsZN4I/CVlPLbOs7rwTe/neSGN39BAp/MuJBr+tbuikdDQ6NhiDHq+d/tA2kaZ2T6okxySiqDev67v2Tz7obD3DW8CxMGasdyRwJ1MihSyjwp5SgpZVfH//mO+09IKUc7bv8upUx3/OstpXw6FIor2GySl1bs4+7Fm+nRJpFl9w0jrZ1We66h0RhomRjDvEkZFJSbuevdLCrN6iq/1h84w+wvdnFpj5YNtvteIzCNejNGucnCfUs289KK/Vw3oB1Lpg2hZaK2K1ZDozGR1q4J/74xnS1HCvnb0t8CVn5lnynj7sWb6ZIcz39v6ofeS1h76tSp7Nrl/xy0zz//PKCMRnA0WoNyvLCCCW/8wjc7TvHo6J78+4Z0rV+PhkYjZXSfNvz5sm58tvk489b+7lOuuNLM1EWZ6AS8NfkCEmO8t06aP38+vXr5PzFSMyihp1EalKzD+Yx7dR1H88t5e/IFTBveReskqqHRyHlg1Plc06cNz36zh1V7Ttd4LDs7mx49ejBg1LWsnTOFputfJTkWVq5cSf/+/enTpw933nknVVVVAIwcOZLMzEwAEhISePTRR0lPT2fIkCGcPn2a9evXs3z5cmbOnEm/fv04ePBgg7/es5FGZ1A+zjzKzXN/JT7awNJ7L+SSHr72UmpoaDQmhBC8cEM6vdsm8cCSrew7XVLj8b1791KaOpK3v1hNattk/vOf/zBlyhQ+/PBDfvvtNywWC2+88YbHuGVlZQwZMoRt27YxfPhw5s2bx4UXXsjYsWN5/vnn2bp1K+edp52VEgoajUGx2iRPf7WLmZ9s54LUZiy7dxjnt0wMt1oaZzlCiKuEEHuFEAeEEB696oSdlx2PbxdCDAiHnmcLsVF65k3KIDZKz9SFmeSXmQD4cvsJ9Ikp3HXD1dw6uBO33XYbK1euJDU1lW7dugEwefJk1qzx3N4WFRXFmDFjABg4cCDZ2dkN9nrONRqFQSmuNHPngk3MW3uIyUM7seCOQTSNiwq3WhpnOUIIPfAacDXQC7hZCOEemL8a6Or4Nx3wvETWCIo2TWKZe/tAThVXcvd7WfxyMI8XvttLtFHPY2P850W8YTQanSFxvV6PxRKe0yPPBepkUIQQNwghdgohbEIIn12EA13l+ePQmTLGv/YzPx84w9Pj03hyXBpGrVOwRsMwCDjgKH03AR9gb4jqyjhgkbSzAWjq6BqhUQf6d2zGv67vw6+H8rnpzXW0bhJLef4pNm38FYAlS5Zw2WWXkZ2dzYEDBwB49913GTFihOo5EhMTKSkpCSyooZq6Nr3ZAVwH/M+XgMtV3uXAMWCTEGK5lDJgeUVplYVrX/sZnYD3pg5mSJcWdVRXQyMo2gFHXf4+BgxWIdMOOOkq5GiKOh3sV8wZGQ1+ikNAcnNzSUlJCbcaNSg9kUPp6ZNEde1KTEwMY8eOpaysjOjoaFJTU4mJiaFv375IKYmPj2f79u28++677N27l9tvv534+HgqKiqc73dBQQFFRUVkZGRQWlrK/PnzuffeeznvvPOIjo4OSrdIfL8c9A7XxHUyKFLK3UCgCivnVZ5DVrnKC2hQDp0pY2RSDPMnZ9CheVxdVNXQqA3evtjumyTUyNRoehofHy+VCqRIIiMjg0jSy2azkX7eeZyxWfjjpNt47/332bGjXro21YpIe78UhBC2cM3dELEjX1dwXnFtXx+ts/HpPRdqxkQjXBwDXA/QaQ+cqIWMRi347NNPiTlzhk+AV//975C1uteoPwIaFCHECiHEDi//3GPJPofwcp/Pb4aUcq6UMkNKmdGtTTMStLbzGuFjE9BVCJEqhIgCbsLeENWV5cAkR7XXEKBIOSNIo/bYbDae/MtfmF1ayjCgV1UVf/7Tn8KtlkYA6tS+XiXaFZxGo0RKaRFC3Ad8B+iBt6WUO4UQMxyPvwl8DYwGDgDlwB2Bxk1OTq4/pevA9Ol+z75rUBTvZLTj7yfKyrjt739n0pQpREVFRoVnJL1fbtT+ZMI6IkLhRgohVgMPSyk9AopCCAOwDxgFHMd+1XeLlHJnoHEzMjIiMtascXYghMiSUjZ4dlz7XvtHyZ3Myc7mGpf7r4iP58b//IepkbuQRwTh+l5D3cuGxwshjgFDga+EEN857ne2r5dSWgDlKm838JEaY6KhoXFu4u6dKDxRVsbTf/87JpMpLHppBCYkHkp9IYQoAfaGWw8vJANnwq2EFzS9gqO7lLLB2y0IIb6VUl7V0PM2BoQQukQ4sARSr/Hy+MVQ+gs8ZJFyXoMr10gI5/cr0g1KZrhcN39oegWHppfXuZ8H/gCYgIPAHVLKwnDo4ooQ4gZgNtATGOQtjF2f6IWY0APe2QEJ3qp5fgauhJwy6ODYbNrgCCHeBsYAOVLKtHDo4A0hRAdgEdAasAFzpZT/bUgdtC3nGhrh4QcgTUrZF3uO8a9h1kdB2azs2RSrnhFC6OLhued8GBOAYUB/iNNXHz0eDhYAkehhWoD/k1L2BIYA93ppFVSvaAZFQyMMSCm/d+QXATZgr34MO1LK3VLKsISZdXBdB0hxz524MwcSYuCfjlLuBkdKuQbID8fc/pBSnpRSbnbcLsGes/a5568+iPRNHnPDrYAPNL2CQ9PLP3cCH4ZbiXDiyJ089xAknAog2wXoCom/2b0ULZfiBSFEZ6A/8GuDzhvJORQNjcaMEGIF9ni2O49KKZc5ZB4FMoDrZAP9GFXqtRofWwHqSae2MbBFB65neBsAI1Dh+NuIfT9QJYAOPiuRMuC+n/rAsWB/GUk5FAUhRALwE/C0lPKzhpw70j0UDY1GS6BNwUKIydiTu6MayphASDYrhxwp5Qmglet9QoihwGwp5ZWOv//qkH224TVsHAghjMCnwOKGNiag5VA0NMKCEOIq4BFgrJSyPNz6RChqWt9oOBD2Lr1vAbullP8Jhw4RbVCEEM8LIfY4TsJbKoRoGm6dQP05MA2oT63Pm6lPhBBvCyFyhBAR0yJWCNFBCPGjEGK34zMMV4OoV4FE4AchxFYhxJth0qMGvjYrh4NI3RQthFgC/AJ0F0IcE0L8Mdw6ORgG3A5c6vhObRVCBKpxCCkRnUMRQlwBrHL0VPoXgJTykTCrhRCiJ/Y67//RgHFmH7rosZedOs+bAW5Wc95MfSOEGA6UYj+AKiJizY7Dr9pIKTcLIRKBLODaSHi/NDQaOxHtoWillapQc6pgWIjE8spIKK3U0DhbiWiD4sadwDfhViICCeq8GY1qwlVaqaFxthL2Kq8gSistwOJI0itCCOq8GQ07jtLKT4EHpZTF4dZHQ+NsIOwGRSutrDPaeTNBEu7SSg2Ns5WIDnlppZWq0EorgyASSis1NM5WItqgoJVWBiRSSyshYssrw15aqaFxthLRZcMaGhoaGo2HkHgogTawCTsvOzbebRdCDAjFvBoaGhoakUOoQl4L8H8+wNVAV8e/6cAbIZpXQ0NDQyNCCIlBUbGBbRz23dJSSrkBaOrYsayhoaGhcZbQUGXDvjbfnXQXFEJMx+7FEB8fP7BHjx4eg/12vAiAPu2aqJo8GHmblOw8Uaxa3myV7DmlXr7KYmPf6RKMOh092gQ+zrzcZOVgbimxRj3nt0wIKF9cYeZwfjlJMQY6tYgPKJ9fZuJ4YQXN46No1zQ2oPzp4kpySqpomRhNq6SYgPLHCyvILzPRtmksLeIDn4d0OK+c4koznZrHkRRrDCh/IKeUCrOV81ISiIvSB5Tfc6oEs9VGt1aJ7Ni25YyUMiXgk0JMcnKy7Ny5c0NPq3GOkJWVFZbvNQBSypD8AzoDO3w89hVwkcvfK4GBgcYcOHCgdMdkscpOj3wpOz3ypcdj3igsMwUlf7ygPCj5faeKZadHvpSps9TJbzlSIDs98qUc9PQPquR/3p8rOz3ypbzm5TWq5L/dcVJ2euRLeec7G1XJv//rYdnpkS/lI59sUyX/4g97ZadHvpQvfLdHlfzfPtsuOz3ypVy4/pAq+WkLN8lOj3wpv9p+QpX8ta+tk50e+VKu3ZerSn7YnJWy0yNfyqzD+RLIlKH57r8N5Pj6/rv/8/a91tAIFaH6XtfmX0OVDYds811JpSWwkAvFleZ6Ht8ub1NZLFfi0EetvHN8mzr5sipFH3UTWByKqJUXjo35quUd+/jN1uDkS1V+DkqbgKIKdZ+zMr5aeZUsIDLPGNfQaFAayqAsByY5qr2GAEVSSo9wlxpK6tlABD9+7fSRKhfkagOkVr5+DZzJagWCMBCOJV+tgdA5Vny1FwK1lg+hQZER2AQzVOw4XsSuE3XvTGOzSX7PLfW43+r44uWXmSgoM3GisIKyKgs5xZWYrTbn4wAnCiucf5dVWSg31fxOFZSZOFNa5VePPaeKqTRb6/pyNHwQkhyKYwPbSCDZseHvCezHdSKlfBP4GhgNHADKgVof2+lqIKSUCOGtlZWrfH0boNoZLLXbf2o7frAGqMqizgVS5NUuyOUm+49XrUegqK1WXjEQwcqH0qCowTU32LFjxwaduzbYbJJZn23no8xjANw98jweucozn6mW9zce4e+f7+D9qYO58PxkALIOF3D9G+v57J4Lue719V6fd0n3FN65YxDHCysYNmeVx+OPjenFmL5taJUUwyX/Xk1huZnsOdd4HWvNvlwmvb2RGwa25/kb0mv9WjR8ExKDIqW8OcDjErg3FHMVV5pJitZx/+Bm7Nq927lA+CLKbGXeWHtB2a5duwkgTpLJ4pTfvXt3QH3ayODkUw12eb1QJ9873sy8sW0w6oUq+Yymdvlog06V/PAUEwPGtiHGqG78K9tZuXhsG2KNUpX8+FTJVe3bEB9lViV/a3c915/XhoToSlXy9w6IZVrfNiTGlHnIx8TE0L59e4xGz+R+iENeAZFSzgXmAmRkZET8buK5a3/no8xjTLs4lcJyM2+sPsjF5yc7jUGwbMq2O3C7ThY7x/himz3qnZVd4PN5P+7NBezFIN74x5e7WPRLNj/NvITCcv+f6Zfb7fN9vvU4c67vi14XYDHQCJqwN4cMluIKC/cPbsaA89pyfpd2RBv9V/YUlJnQF9jbgHVrk4RB7z/Kl1dahbGwAoDu7ZoENFg5JZVEFdm/7D3aNQnoMZ0qqiSnpBK9TtCzbeCqsBOFFZwprSJKr6NHm6SA8scKyskvMxFj1NOtVeAqsiN55RRWmIiLMqiqIss+U0ZxpZmEaANdUgLLH8wtpazKQlKMkc7JgavO9ueUUGGy0jQuio7N4wLK606VUGWx0iI+inbNquWllOTl5XHs2DFSU1Od9yvhjoY2KI2Jk0UV/HfFfq7o1Yq/je6JyWpjzf5cXll1oNYGxWy1e8C5XkJSugAL+497c7jjnU0+Hz+SX7PNn80mvY6591SJQxfJgZxSurcO/PvQCI5I7+XlQUmlmU5NjRjiElXF/a0uoR+rijBQDXkVE9hcZNREmZRQlM0mVeVRlPHV6OIqp1pe1u/4Tv1VhuCU4gPV4/vQXwhBixYtqKyseWVbHbILLpR4LvHG6oNYbDYeG9MLIQTRBj2Thnbml9/zOJpfux6t+WUmAM6UmJz3KRdrtgCf9adZx/w+7v7VKjN5frZSSg7mlnGRwyAqWwnONixWldU79UQjNCgWBAIhRNALvhp5a7DyLiLBjC9Rl+dwLvhSpQGS6nWBWhgsGZyBqG+DpchZvMi7e4tWm6TUUQUXSg8lQptg1oqicjMfZx5jbHo7Orh4iH/o2xaAb3ecqtW4eaV2Q5JXVu2hSHx/dsHiupCWVXkm3c+UmiitsjCyewp6neBwXlmd54wkpJR889tJLn9xTVj1aJQGRaG+PY6g5dXoU98GzqUMOCiDVU8eU60Nlpr33uU1qnmtijGB0BoUKeXNUso2UkqjlLK9lPKtkA3ewHyw6QgVZit/vCi1xv0dW8TRrVUCaw+cqdW4iodS7rLYWxxXY+YAV9UHcwMv/iaXMVw/Z4WcErun2r5ZLG2bxnA47+w5DeNATim3v7WRuxdvJipASL++aYQGpXohCHZBDuRaQ02Pw98ideGFF3qM767P2rVr6d27N/369eP48eNMmDChxpje9B89ejSFhYUUFhby+uuvB5T30N9F5p133uG+++4D4M0332TRokU+5SUyqBCiLwP0+eefs2vXLvuYUvLyc0+zYe1q1cZZunlAyvucnZ3N+++/7yFf/ToC6+763Ql2f9K5gJSSDzOPMqhzc3q19czXDenSgszs/IAGwB2bTZJfbjcoruEoxUOpCFDGu/tk4LJl1zL2Mi8G5YzDQ0pOiKZj8ziPvEtjRErJOz8fYvTLa9l+rJAnx/bmqwcuCqtOjc6gFFeaUfJtahdYJVZrUelBKPL+xl+/fn1A+cWLF/Pwww+zdetW2rVrxyeffBJw/K+//pqmTZs6DYrVVh1rVuMB2WT1+K7Dz5gxg0mTJnnKq3y9YP8C19A/gEGxSbjn4b9x4fBLVHlMyng6l3Cm8j57Myje5P2h5E3io/RaUt4Lvx0v4vfcMq4b0M7r40O6tKDcZA06/1BhtjrzHK6LvbJHKRT7Qlw/f28GKs9RDNAiIZqOzeMbvUEpKDNxx4JNPPnFLi46P5mV/zeSyRd2Dlh0VN80uiqvkkoLep39TbPaJE9+sdPvxqtKx5fZJiVRBh3GAG94pdlKl5QE7hjW2e8ilZCQQGlpKevXreHVF54lqVlzDu/fwwUXZPDee+/x1ltv8dFHH/Hdd9+xYsUKnn76acaMGcMnK9bz5SdL+OHbrxCWKg5nH2L8+PE899xzAHTu3JnMzExmzZrFwYMHGTdqGMNGXMKf/vYk/3nhBZYt/ZSqqirGjx/Pk08+6aHXJ0ve5e3XXqRFSiv69OpBfJy9P9fs2bNJSEjg4Ycf5uWXX+bNN9/EYDDQtvP5vDxvIfmFRfzxjw+ydXMWQgieeOIJrr/+epYsWcIzzzyDlJKrR4/m9gf+RpRBR78ubXjggfv55uuviY2NZdmyZRw8eJDly5fz008/8c9//pMPPvqYxx59glFXXs3Iq/5Al9RUJk+ezBdffIHZbObjjz+mR48eTt3u/9OfAbhu1FD++84H9G7Th6SkREpLS5k1axa7d++mX79+TJ48mc8++4zn//MicW3OJ8qgY+KYy1kw/3+kp/veX6B4KO2axXKyyHsZ6rnM0i3HidLruLqP976tAzs1A2Db0UIGdGymelxlLxJAmcttxUNRuwfKHxaXVhImL+OdcRqUKNo3iyW/zESl2UpMgCrRSORATilTF27iRGElT43rze1DOgWsLm0oGqVBESIavU6oumKXONptSFATxZcQlAckpWTXju18tnI96d27MO7KS/n555+ZOnUq69atY8yYMUyYMIHs7GzA7hHodYK9u35j3S+baN08ke7du3P//ffToUN1d5o5c+awY8cOlny3loRoA9999x0HDuxn48aNSCkZO3Ysa9asYfjw4c7nHD9xgtf//SxfrlwHUXHce+u1ZAz0PHpmzpw5HDp0CL3RyIbdR4nS6/jff58nKSmJ3377DYCCggJOnDjBI488QlZWFs2aNePyy6+g3bdfMe7aa6koLyPjgsHMefZZ/vKXvzBv3jz+/ve/M3bsWOdrVq48lXp/CSQnJ7N582Zef/11XnjhBebPn+/US/k8ld+G6+c7Z84cXnjhBb788ksAmjdvzrsLF3HXrKc4ln0QU1UVaX36+v2slPxb+2Zx7DvtuWv7XMZitfHFtpNc2qMlTXw05WyZGE1yQpSzeapalO9Bszgj5S4eivLzMofCoLiEvLyF5PJKTUQZdCRGG2iZGA1ATnEVHVsELk2PJDZl53Pngk1E6XUsmT6YgZ2ah1ulGjRCg2JGpxgUm+SJP/T2K7/3VAkxRh1lVVaaxBpq7FXwxu6TxSREGyiqMKtO+vcfMJBWbdohEfTr14/s7Gwuush7LNNqA4NOMHjYCOITk4iJiaZXr14cPny4hkFRkFISpdfxy5of+XHlCvr37w9AaWkp+/fvr2FQNmzYQMbQi2jduhV5pVWMv34CR7N/9xizb9++3HrrrYz5w1h6DLkUo0HHr+t+YvHi6pBSs2bNWLNmDSNHjiQlxd64dOLNN/Pzr+u54frrMEZFceXV9pNzBw4cyA8//ODltdrfP4Ou2iu87rrrnM/57LPPvMoroRB/Bv2GG27gyaf+wZ3/9xifLnmPsTfejNVhrH1RUmX3UNo3C9xV+VxjY3Y+Z0qrGNevrU8ZIQS92jZhR5AhL8VDSU6IZn9OqXOfiJIDCzYn4w3X74p3D8VEcnwUQghnl+zTJZWNyqD8cjCPOxdsok3TGBbdOYj2AdaycNAIDYoFnRDo1ZYNS4leCKcBCijvWJT0OoE1QL8qe4dNiI6Jceqj1+uxWHzvcZDYx4+KinLq4+s5yuxRBoGUkgcemsnDf7rPz2u1/+ij9EqOw7vcV199xZo1a/hs6efMfuof/JK5xZ4fcZN3T7orC0CUQWAwGJ1XmL70V3ImzqiChOjoaI/nGAwGbDabc/yqKns4yt/nFRcXx4hLLmX191/z5bJPeW/5qoCfr+KhqGnTf66xcncOUXodw7v573reu20S6w+cocpiJdqgLlyk5DSaOY4vMFltxOj0znJhtX3h/OFaemzy5qGUVdEiwf7dcxoUH7vvI5FfDuZxx4KNdGwex+KpQ0hxeFmRRqNMyguBagNhdVwN6XUiYL27fVF1MSgBPBSbtEeBBer0UR7VCQLuo0lMTKSkxL6z16DXMWzkKN5/dyGlpfZQzfHjx8nJyanxnIEZF5D5yzpKCgswm80sX/qpp842G0ePHuWSSy7hqWfmUFJchKminKHDL2Hem9UHaRYUFDB48GB++uknzpw5g9Vq5aOPPiRjyDBnHsrb++Oqt9NgOjwUX6+2c+fObN68GauU7P5tG0cOZ9d4vvu4CrdNuoN/PT6LgQMzaNKsWcDPS+nfFYlXduFESsnK3acZel4L4qP9X2P2aJ2IxSbJPqM+qa00cWzqCKUpORPl8/VmAILF6pJD8ZaTKa4wO0N5rZLsi/HpYv+NJCOFPaeKmb4okw7N4lgyLXKNCTRKg+LwUFQs4EplkWIgAtkf16ohNR6Q83cg7O0jAho4x8M6IRDCf9VWixYtGDL0Qq4bNZQnHv0rF48cxbjrb2To0KH06dOHCRMmeCywLVu1ZsafZ3HFpSO46+ZrSevbz1Nnq5XbbruNPn36MGxwBrdNvZuU5OZMf+BhCgoLSEtLIz09nR9//JE2bdrw7LPPcskll5Cenk7f9H5ccuVoogzVRRHu3HTTTTz//PP079+fAwcOAGDQK2Eo76/3+uuvJz8/n+FDB/HRu2/TtWs3+/gu70/fvn0xGAykp6fz4osvApDWrz/xiYlMmjzFpz6ulFRaiDboSE4IfNDXucTB3DKy88q5rGfLgLJdku3tdg6dUZ+DUnIoTeMUg2L/2xLCkJerl+Mt5FVaZSHBYSybxBqJMujIaQQeyonCCqa8vYm4aD0L7xzk9LIilUYV8qqyWDFZbOgUDyWQB6FcITsMhCnAoSJOeYcB8vdFLy0tpdJs5YKhF3H9NVeQV2bCapO8+uqrTpkFCxY4b3fu3JmNm7dyMLeU2yZN5vJrJzoXQCXRDDiT9wBvLVjEoTNldElJ4HhBBVOm38MTf53pUyerhGsn3srM++8iO6+cxGgD7R27nWfPnu2UW7duHQAF5SaO5pejF4LExEReen0ebd3CQbfccgu33HILYC+9PF5YgVGv49e9x536T5gwgQkTJgAwbNgwZ9lwbkkl/3jxdXq1SWLXyWIyf9tLsiPckJGRwerVqwGIjY3l+++/51RxJTnFlfRsk8Tuk8X2ne0Oj8xoNLJy5coauh0/fhxps3HVlVewL6csoEEprrSQGGOkSVzgkyDPJVbtOQ3ApT1bBZRNTbH3Y1Oz2VBByaE0jbMb8iqz4qHY/w91DsXbeKWVFhJi7MudPY8SHfEhryqLlRnvZVFaZeHjGUM9fpuRSKPyUJQYuHB4KIE2Kjo9Dp1Arwt8Bat8D/UqQ2pWVwMk1IXInPJqPCxXgxiEPjqVIbsa8mo8Mumqj7r3UwkHCjXvj6063+WqnzcWLVrEmMtG8KdZj2FwxPIDeyhmkmIMPquYzlVW7M6hR+tEVbmlBEeV1O9BGJQKk7uHYv+hKZVZ3jyKYKmRQ/EyXkmlhcSY6uvnVokxER/y+seXu9h+rIj/3JhOTxWNYSOBRuWhKAZFJ+yLmk1Kn51FwdPjUHZ3+6rZtslaLsjKgm8OwgDpBGaz/x+S4sXrdThyQAE8LNcFX4WB8Pb+BNLf3kdNMbh+xbFK+2cjgjCIOp1AJ0TAzYqTJk1ixDXXU2m2oVe58VNZVJJiNIOiUFhuIutwAXePOE/1c7qkxAcV8lKS8k1jHR6KI+RVnUMJQVLe5cvoblBsNkmpyUKiS36oRUIUh85Ebj+vZVuP896GI9w1vAtX9G4dbnVU08g8FHtSVQhRvVfEzyLi7nEEai/iuuDrXAyQL4L1OGrkaIL1OFTldILXR1m81XpMirehUxlyVBZ7VQZOVpf9qn1/9C4Gy91jdf/sSirNJMUaiYvSY9DOwgBg9d5crDbJKBX5E4XU5AR+D2IxVjyUZg4PRVnwnTmUUHsoblc65Y7NzQkxrgYl2tlfLNI4UVjB35fu4ILOzZh5ZfdwqxMUITEoQoirhBB7hRAHhBCzvDw+UghRJITY6vj3eG3mcbYc1xspKy50tgLxhdVtwQf/YRGr2xV+QHm3BTxQexEPjyBAB+GaISaVV/giSI9AuCzgAT0yULaUqDVwumANhFAv788AKeehxMTEOO8rdngoQggt7OVgxe7TJCdEkd6+qerndG4RR2G5WXU/NCWH0sRHlVd970NRLkQTXTzT5Pgo8h15z0hCSsmsz37DYpP8+4Z+YW+lEix1DnkJIfTAa8DlwDFgkxBiuZRyl5voWinlmLrMpXwxopNSyM3PJb+kFApjnFVH7pSbLOSXmRGF0ZitkrwyExRG+2y/UlplobDcjK4ohkqzlYJy+3N9fagllWaKKiwYimMoM1mdz/W1ua64wkxJpQVjSazzufqiGJ8huMJyM+UmC3tKYp3P1RX5jnMXlJuoNNvYXRzjfK4s9C2fX2bCbLUhimKcz7Xmx/iUP1NaZW/gWBDjfK45z7d8bok9Rm3Nj3Y+t+qMb/mc4kp0OoEpL9r53Ipc31Utp4srMep1VOZGkVNsP7Ss9HS1vHJio0JJpZnEaPuikqQZFMxWGz/ty+Wq3q0DHnLlSjvHxtDjBRUktQn8PtpbnOiIibLnupw5lBAm5f15KKWOUHlCdE0PxSbtIb9Iqpz6KPMoa/bl8uTY3o1q06VCKHIog4ADUsrfAYQQHwDjAHeDUmeUHEpSfAwm2ZLpiw/w9pQMLu3hvTrlnZ8P8eQXu9j82OXsPlnMtCW/8uH0IfTt0sKr/Gs/HuD5746w959X8dPeXKYvz+LL+y+iZ7smXuX/9e0e5q05xv6nr+bzrcf58/JtrPq/ET5PMnxi2Q6Wbsll++wreW/DYf6+fAe//m2Uc6OVOw9/vI2fDxTwy18HMHfNQZ75eg+/zb6ixpWWK/cszmLf6VJWPNSfF3/Yx39X7ufgM6N9GrjJb2+ksNzEsvv68/RXu3hvwxF2/+Mqr7IAE95Yj1GvY8n0/vz1s9/4YVcOmX+/3Kf8Q/9dS7umMcyf3I8/fbCFLUcKWfOXS3zKz3j+R9LbN+Xlm3vy74WZHCso59sHh/uUv/2fK7i8Vyueva4n/5i3AZPFxid39/Mp75qY1QyKvY1HSaWFUSqqu1xRkvfHCypUJYvLTVZijXqiHRd+VeaaOZSQbGz0k0MpcbR7qRnysudz8soix6DklVbxz692Mzi1ObcP6RRudWpFKPypdsBRl7+POe5zZ6gQYpsQ4hshhM9+KUKI6UKITCFEZm5ubo3HFBc7KdbodJ/9dY1VDFCiS2WPP/niSjNRBh3RBr3K8c0eIZRA+ijGQO34SUHJVy+YinyJn7CEXf/q8SvMVr8VN+7jF1WY/Ybs3McP1OG3pNJCUmz1+MUB5e1VW2rGt1htlJusHu//uYyyO/7irsEd66sYlBNFFarkK8xW4qIM1QbFLYcSio2N/qq8nBeiLgaluWPXvnLwVyTwwvd7qTBZeXp8WlAeYyQRCoPi7ZW7rzKbgU5SynTgFeBzX4NJKedKKTOklBlKDymFEhfXVVkQ/B3lWlJpJtaox6jXqV6QnQt4nEr5WPULfnGQ8t4MhL/XW+yqv9rXG2sIQt5cQ3+zVfo9y8L+froYiEqzz1JvKaWHASqu9P1aqyxWqiy2Gvr40105dEl5va6Ly7lIMLvj3UlOiCZKr+N4gUqDYrKHvJRWLfWeQ/EZ8nLJoTi8EtcTJMPJjuNFfLDpKJMv7Mz5LRvvWfehMCjHANeuhu2BE64CUspiKWWp4/bXgFEIEdxlEXYPIiHagF4nnAuJ3wW8wiXE4Vio/C1SxRXVV7xOeb/jmz0XfH/jV3qRD+AxuYdo/C74FZ7y/g2QZ07BX6K1OAgD581ASAmlXs77Bqg02zBbZY3xS6ssPs/IdvU+Ff396l6hyGseCsDvZ+y744Op7lLQ6QRtm8ZwrLC2Hopjp7xyYmMIqrwUo6QTnh5KqaMpaI2QVwR5KFJKZi/fSYv4KP50Wddwq1MnQmFQNgFdhRCpQogo4CZguauAEKK1cGSehRCDHPPmBTuR6xW7Ua8jLsBBSSVV1QusPTQVvEcQUF5ZkGPUekBBGAiXEJma8YuDDql55hR8ySvnsTv1cVzp+5IvM1mxSTwMelG5d3n3ShxlfF8G2t2gNIk1Umm2ORcrd4qd49f8fM9VVu527I7vEbxBAXtiXq2HUm6yOHIoDg/F7OahhKDSShkrLsrgM+TlmpRvGheFTlQfvBVOVu3JIfNwAQ9d3r3R75Gqs0GRUlqA+4DvgN3AR1LKnUKIGUKIGQ6xCcAOIcQ24GXgJqnmAHM3Slyu2CFwnN11QdbpBInRhgDy1VfUyl6FYBdktePXOuQVICeSpNIg+sop+JJ3hoxUju9pIPzLF1d6H9/X++kcP1rd+N48mnMZZXd8bRtltmsaywnVHoqN2Cg90Ub3HIqyYz50OZTYKL2HgVK+u64GRa8TNIuLsld+hhGbTfLv7/fRqUUcN2S0D/yECCckgWRHGOtrt/vedLn9KvCq+/OCxTXHAYHj5sWVlhpXokkqDJBScSWECBhGcTUQMY4qlsAGrmYMP1DIyJkjiPO/wJosNqosNpcFU7nC928gPEKCAQ2E2gXf04Pwp48yvtockIeBcHk/vYWgfY1/LlKb3fHutG0aS05Jlao29hUmC22bxDjL9ZXwlOJVhGIriDJWrFHvEUKrMFuJMug8qh1bJESFPeT17c5T7DpZzIsT0wOeJtsYaFSvwL0fT1KMf4PizaNR6xFUy/tL+rvp42f86pyCXd6g1/k927zKUjOnkBDlP2Tn7hEEuyCrNxDqQnDuBshp4IL0IOr79Z6L/LTPvjv+0lrkTxTaNLFfeOWo6IdVYbaXDSsbepWQlJoD7NSieDmxRr1Hkr/SUbbsTov46LAm5a02yX9+2Mf5LRMYm+6tMLbx0agMSrGLRwD+F3CwX60mBWFQil3KdAONb7VJSqosNUIn/sZXcgpqPSxl4XUN2fkzoM6QkWPhtle3+Q7ZKfcr+gfKiSj6qPUgFM9LbVVbcZAGonp8dQaxWPNQnKzYnUNyQhT9gtgd707LRIdBKVFhUExWYh2bGqP0OmcVVqAD7IJBCXnFRHkaFMWguRNuD+X7nac4kFPKg5d19XvSaGOiURkUbx5EiY+krV3ezQD5WZDdcwp2eYPqnIKij9oravBvsNxzCsr4anMKQvg3QO5X7NEGPTFGXdA5iEAGQn3OJbgQmbsBUptDSXAL8Z1rmK02Vu/N4ZLuLeu010E55Cm3JHAL+AoXD8GoFx69vEKBMylv1Hs0myw3WYmL8uahhC+HIqXkf2t+p1OLOK5OaxMWHeqDRmNQ3MtQwf8C7swpRLstyCpzCop8icqQTqDx3RfMwPLex1e7IDvHD6C/u8fkK6ejnMeujK9XihwCvl77+AnRBnTCd87IV44moIGIVmeAXPckucqfa2w6ZN8df1mv4HbHu9PScephIA9FSkm5uXpBjzLoqz2UEBqUGkl595CX2UqMVw8lmqIKc0ja5wfLpuwCth4tZOpFqWeNdwKNyKAoOQUlxAH2cIevvQrOBdM1JBUX/IIcWF6dR+PNQPgrEqje3evq0agZX50HFJLXG8T4SpGDP3mdgPgoxWPSEaX37zEpe5IU3cFfWbKnd3susmJ3DlGG4HfHu9MiPhqdCJxDqbLYkBJnH69og65ePBR/OZQKc3XIzRWl/Uo4ug7PXXOQ5vFRTBjYIbBwI6LRGBT3EAe4thfxvOr1tWD62qvga3xf7UV8Lsg+FrRiLwtysAu4v93jxbX0UILXR61BNKPXiRqx60DjJ0QbnGEYZ5WdH4/GVZdA+5LcDYrrJrdzBSklK3afZth5LYiLqtvr1+sEyQmBTz1UWtfHeQl51YeHEuOtystPUh4afrf8gZxSVuzO4fYhnbwausZM4zEoFd5zCuA9LOLNQPgr1XVP8tpvG7HYvLcXcU9SK/qUVFm8thdR5JvEqlvw3TfiKfIBk+buZdJBGKCkGN8huOIKe5+zmBoGwuBzwS927Ilx7aTsL8Rn7zpQ02uwj+/7/XHPg/jV36UEGzirwgxqOZBTypH88jqHuxRaJcUEDHkpvx1nUt6gc3oQgQ6MCwaLVaITEG3UeeRQlH0w7iQnhGe3/OJfD2PUC25rpA0g/dFoDIrXkJGf0lVfV9S+5b0bCJ/yVV70cbQXUbqbetenZpFAmcnqtZeRr9erNqdg199/iMw1p2CX918k4N7/KpA+7gYiUJVaovv4ATwad/lA+vvq0nyu8INjd/woH925g6VlYnRAg6KchRLr8IiiXEJeofRQzFYbBp09TOoR8nLs1HenRRj6eVWYrHyadYyr0to4CxvOJhqRQfESAorznYj1ldQG9QZInXxNjwC8l656H9/33oySSgvCJaegjG+y2Kj04jG55xQU/f2F7IJbwL15EKFb8Eu8eByBihyCHd9d/lxj5e4c+rRrQusmvs+kCYaWSdEBq7yU72p1lVd12XAocyhVFht6ncCoF0GVDUPDeihfbj9BcaWFWwd3bLA5G5JG8wvzlqT2H/LyltT2t+B7C5H5TvQGMkDuqTavOQWXjsbuZzLY+4QZapR2uu61cK9a8bZgJsUYsdokZSZrDc/FOb4Xg1JSaXEerRtIPtgFPFARQtumNRe6JrFGn+d+l1SaSU2OdxvfwPFC7wtciRcPyxdZWVktDQbDfCCNerjoeu6559i9e3eoh/WL1Sa5Oz2GxFhDyOa+tjOMat2CXbt2+TwkzmKxMW9sG1Isuezenc+fM+yf2a5du5n7h9CVy8ZHW7g4pSXx0RYGX92yxmt85pLmxEXh9XXPH9eGhOjSBvs8WtqqWDC+LU2qcti9O6de5vjhhx/6bNu2LbteBgcbsMNisUwdOHCgxwtoRAaldiEvbwbIu0fj20B4y0MUV5qJ0tfMKfhrX6IsyK4/PH8dkN03cULNkF1Lt0O5fC34ij7uBsXb+Ip8aaXFaeyqx/duIJQzVNxPzSyptNCheZybvD3nIqX0WIDsjTxr9kwJHFLz1Gf3yRIf8p6v1xcGg2F+69ate6akpBTodLqQnxG7a9euTj179gz1sH7JLzNhKyina8sEZ/ipruSVVnG8sIKurZMw+jg1taTSjO5MGeelJBAfbSA6txSbhC4p8ViOF4VED4BmcVGUVNovzE4XV9KjXRPnd8xyvIjkhCjaNPFyeunJYhKiDR7f1fqgwmTBnFNK2yaxJNdjuMtqtVrS0tLO1MfYNptN5Obm9jp16tR8YKz7440w5KU2JGW/L0GtfJWFGKPOI6fge3zfC7gvfYKTD3L8Kt8GIjT6V3dWdpf3ZaC9jW+y2qg0e8sZeZcvrvA8Q8W+J8kzJ+KryMEeJqy5JykAaSkpKcX1YUzCRXGFGaPbBVBdUY7GNvtJrtsc4VadY3HXCWEPwYb4nbVPI5yHM0nn/RIppXN+dww6EdLQmz/yy8zohKBpnLoLm0hEp9PJlJSUIuzeu+fjDaxPrSmuNHvkFGKM9iScr6qt+Ch9jdCNvxCW/SwUd4/AdzuS4oqaVUPg/1Au18OvnPL+QnZe9PG3gLu3mbHr778Kzl1/5fm+5dUbIH/6u8tXGwjP8W0SytzOUKmy2DBZbR76JMXYq+zck73e9iQFQHc2GROrzUZJlb1Rqq/QVG0w6h0egJ8WKspHofwMhbAv9jLEFkUiEcI+PigGxtOguWPQ60JabeYLm5QUVphIijE4DXFjxfHb8PoiGs0rc9+nAP43y3kLcUQZdMQa9aqvqBNVhLBc8X/F7ttDCXZ8ta830PjuBiiwx6FufJtNUmpSP365yYrVJlV7WN5Kwl3l3Y899ubdnkuUVNrDjKHezGlw/Bb9XeEr3qXr/iK71xBSVbBJ+9GxisFUClHcDZo7Bp0IaU8xXyi5yaZxUfU+VzhpNAbF274DUOLyPhbMWM8FxFci2VtOQa8TJPrY/e7NQCgeke8Qk++ciId8lR8PwkeRQJ0NkA8Pq7rPmbsH5N2jKTVZkNJzwfeV8/KW7/I3vrczwsH36/VWkdeYmD17Ni+88AIAjz/+OCtWrAjq+UWOcJe3fla+WLBgASdOVB+8OnXqVHbt2lVDRq+zLx/+rvA9PBTHfcoSHiqPyZ6XwyPkpXgop06dZMKECR7PM+jtIS/FAD3zzDNBz/3xxx/Ts2dPLrnkEp8yheUmDDpd0Bc1y5cvZ86cOUDN78GUKVP45JNP/D735ZdfbpGdne380k+cOLFTVlZWaEr8fBASgyKEuEoIsVcIcUAIMcvL40II8bLj8e1CiAHBzuFtwQTfpaXecgqKvNqcgnN8lTkFe0NGXwbIc/wYo54oH2eo+Eo6g/ckfjAGy2z1nlPwtSBX9zlT50H48gh8tUfxVnDhqr97SDPg+B76eB8/VOTn5ydt3749bfv27WnHjh1rXS+TOHjqqae47LLLVMtbbdJxcRVcuMvdoMyfP59evXrVkNHrBDohAoS87I8pcwtBDe8kVAE4Zw7FzUNR5mrftq3XBdigE9ikdBq+2hiUt956i9dff50ff/zR6+MWq43iSgtN44IPOY4dO5ZZszyWVFW89957yUeOHHH+aD/88MPDAwcODNzNsw7U2aAIIfTAa8DVQC/gZiFELzexq4Gujn/TgTeCncfbPgUI3kD46ofla3xflUb+DJC3M1R8eVje9PeVUzD6OEOl0mzFZLV5yCdG289QcTdYvhbkQB6E7wXf1wKuNoRVWwOhzoD6Gj8USCk5duxYx65du+5LS0vbWVhY2LysrKzOV4FPP/003bt357LLLmPv3r3O+12vTGfNmkWvXr3o27cvDz/8MACnT59m/PjxpKenk56ezorVa7BJyaK5r5KWlkZaWhovvfQSANnZ2fTs2ZNp06bRu3dvrrjiCioqKvjkk0/IzMzk1ltvpV+/flRUVDBy5EgyMzMBSEhI4NFHHyU9PZ1bx17GqVOnPHRT5JSF/ZG//IW0tDQuHzaIr5d9ipSw6Zd13Dt5olP+mb/PZNlH7wPw0rOzGX/pECZcPox//+Mxj/fnjf/M4W9/uoupE8fyh4sH8sG77zjyJ5L//PMx+qen06dPHz768EMAjhw5TFqaPY+8YMECrrvuOq666iqG9E/jxacfx2KzMWvWLCoqKujXrx+33nqrx5xLliyhT58+pKWl8cgjjwB2A79u3TpmzJjBzJkza8hLKZk5cyZ9+vThulFDWfnVUgBOnjzJ8OHD6devH2lpaaxduxaAb7/9lgEDBpCens6oUaOcut53332+vygOHS644ALS0tKYPn06UkreeeedZjt27IibNGlSlx49evQqLS0VgwYN6r5mzZo4gP/973/Nu3Xr1qtr166977777nbKWHFxcf3vv//+dt27d++Vnp7e4+jRo0H9aELxCxsEHJBS/g4ghPgAGAe4+sfjgEWOY383CCGaCiHaSClPqp2kuMLiPNTHlaQY73sViivMdGoR73F/k1gjJ7zsVSj2EyLzmgT3ktQG73stbI7z2L3tg0iK8ezY6yunoIzvK6fgro/z2GM3j8ZbmxaoPvbYXX/n2Sk+Qlie43u2sXGdz/31+sqJ+CrDrm7D4yun46aPly4Iapn5ybYO+06V+KwptdlsOpvFbDCsKU4FsFrMesjtqjcYfe6W65wo+a/7JZcLWVlZfPDBB2zZsgWLxcKAAQMYOHBgDZn8/HyWLl3Knj17EEJQWFgIwAMPPMCIESNYunQpVquVXUdOs2fHNt5/dxG//vorUkoGDx7MiBEjaNasGfv372fJkiXMmzePG2+8kU8//ZTbbruNV199lRdeeIGMjAwP/crKyhgyZAhPP/000+57kMUL32HAs095f38krPpmOVu3bmXbtm3s/P0oV468mAljLgeqk+iuFBUUsOrbr1i2eiNCCIqLvJcX79+9k3eX/UBFRTk3XT2CEZddyYadW9i7awcbszZTWlRARsYFLPh8AK0Tan4Xt27dypYtWzBJHelpvcj+vz8zZ84cXn31VbZu3eox14kTJ3jkkUfIysqiWbNmXHHFFXz++ec8/vjjrFq1yut79dlnn7F161aWrVzPmTNnuOmaS7js0kt4//33ufLKK3n00UexWq2Ul5eTm5vLtGnTWLNmDampqeTn53t9zd647777ePzxxwG4/fbb+fHHH/UPPPBAwRtvvNHyhRdeODp8+PByV/ns7Gzj7Nmz22VlZe1OSUmxXHzxxd3efffdprfffnthRUWFbujQoaWvvPLK8RkzZrR/5ZVXUp577jnV63QoQl7tgKMufx9z3BesDABCiOlCiEwhRGZubq7zfnsIK8iQlFcPRX0SXxlfbU7Bl7yvnIIveX9JZG+v11dOAbx3WPaVUxBC+NXHfXzl2GPVHoSPKjKfOREfnRB8hbACheDq5QwUKXVCCGcSwX5beiyTNqvFaDFVxllMlXG2AFVFa9euZfz48cTFxZGUlMTYsR7l/iQlJRETE8PUqVP57LPPiIuz27xVq1Zx9913O5TRIaLi2bVlI+PHjyc+Pp6EhASuu+4651Vxamoq/fr1A2DgwIFkZ2cHfMlRUVGMGTMGgPT0/hw5ctinrE1Ktmz6lZtvvhm9Xk/Llq3JGHIhWZsyfT4nPjGR6OhoZs98gBXffEFsrJf9I8DIK0YTExtLs+YtGHzhxfy2JYsNv6znqrHXo9PpaNWqFcMuvpid2zZ7JOVHjRpFkyZNSIiLpUvX7hw67Ps1AGzatImRI0eSkpKCwWDg1ltvZc2aNX6fs27dOm68cSKVVsl5ndoxYsQINm3axAUXXMA777zD7Nmz+e2330hMTGTDhg0MHz6c1NRUAJo3b+53bFd+/PFHBg8eTJ8+fVi1ahUHDhzwu66vW7cufsiQISVt27a1GI1GJk6cmP/TTz8lABiNRnnTTTcVAQwcOLDs8OHDQVURhMJD8RYUdA+qqpGx3ynlXGAuQEZGhlPGW44Aqjvwum+W82lQYjwXZF85BfAeIvOVU7DLGzleUFHjvkAGIre0yk3e+4KsjB+MAfIWsvOXU/A+vm99vHVY9qWPQa8jIdrz/fQ1fkKU/QwV9SE4XwbLc0+SWp6fkH7U3+NnzpxpVlRUlHTeeecdBsjJyWleVlYWn5qa6vN5u3btGujrMYVAsXaDwcDGjRtZuXIlH3zwAa+++iqrVq2qIVNcacYmJTFGHWUV3seJjq7eYKfX66mo8CHogtFYnQswGA1YLBanToqxlFJiMpmw2aiRNKkuG7bPJ12Mq6mqyjnO4i9W8uvPP/Ht8s/4YME85n+43EMP1/dIKn+75U6ksyig5vupvG69TodOr6fK5L/9irf2RYFQzoKBmsclDB8+nDVr1vDVV19x++23M3PmTJo2bVqrAoXKykruueceMjMz6dChA7Nnz3aGIP3p5QuDwSB1jmILg8GAxWIJSqlQeCjHoEankfbAiVrI+ETJKfgKSVkdISUFJafgK2fhvlchsEegLinslPd5Re3DIHqEaPx4HLVY8D1CRv4MkJcOxbV/vcG9n+6fr07nvSy8xMueJKg+9thTH889SaEiKirKZDabnVdxJpMpymg0errAQTB8+HCWLl1KRUUFJSUlfPHFFx4ypaWlFBUVMXr0aF566SVnmGbUqFG88YY9RZlXUompvJTLLhnJ559/Tnl5OWVlZSxdupSLL77Yrw6JiYmUlHjvOuCK3pHUllLSuXNnsrKyAFi2bBlms92gXTB0GB9++CFWq5W8vFw2/7qeAQMzaNu+Awf37cVUVUVJcRG//vwTAOVlpZSUFHPxpVfwlyeeZe/O37zOvfr7r6mqrKSwIJ+N69fRd8BAhl10Ed99sRSL1Upubi7rf15HWr+BXkNrYK/yAlDsmtFoxGz2/PgGDx7MTz/9xJkzZ7BarSxZsoQRI0b4fW+GDx/OJx9/jFEHJYX5rFmzhkGDBnH48GFatmzJtGnT+OMf/8jmzZsZOnQoP/30E4cOHQJQHfKqrLSH75OTkyktLXXPYVmLioo8SvuGDx9e9uuvvyaePHnSYLFY+Pjjj5uPHDmyVNWEAQiFh7IJ6CqESAWOAzcBt7jJLAfuc+RXBgNFweRPKsz+cgrVV6XK435DQC57FZSa8EALsnt7EffjbWvoE1PdkFG54vDrQQRpILx5WIEW/AM5pW7yvnMK/g2WOv299VFT8FaG7a3PmXN8Lx5WsZc9SeAvZKe+7UqwJCQklFVVVcVUVFRERUdHmwsLC5unpqb+XpcxBwwYwMSJE+nXrx+dOnXyuviXlJQwbtw4KisrkVLy4osvAvDf//6X6dOnM/+tt7DY4IWXXmHM5SOZMmUKgwYNAuwlwP379/cb3poyZQozZswgNjaWX375xaec3uFy2KRk2rRpjBs3jkGDBjFq1Cji4+OxScmV14zl8O6tpKenY5Pw4N+epGWrVpTqE7h63HgmXHERHVO70KN3XwDKSkv50x9vxVRlf20zn/BeeZXWbyD3TZnIqePHmPHgTFq3bkOfrp35YfVahlwwAL1OxxP/eJrklq0QVd4XaJ0QCARWx1X79OnT6du3LwMGDGDx4sVOuTZt2vDss89yySWXIKVk9OjRjBs3zuf7AjBm7DiWf/8j4y8bhlGv47nnnqN169YsXLiQ559/HqPRSEJCAosWLSIlJYW5c+dy3XXXYbPZaNmyJT/88IPf8QGaNm3KtGnT6NOnD507d+aCCy5wPjZp0qQz999/f6eZM2faMjMznc3KOnXqZH788cePjxgxopuUUowaNarotttuKww4mQrqbFCklBYhxH3Ad4AeeFtKuVMIMcPx+JvA18Bo4ABQDtwRzBzKFa2vBdMp08whHyBkBHYDpBiU6iSyf3ml3bSvpLCij9lqP0NFOcTIVxJckS+utLcXURbIQB6KzyS7yio1fwt+UoyBo/nlXuV9GVz3Q5aUPmfRXvo7efWYKjz7nPmV91Exp7wmj9db4d27DQU6nY4OHToc2b9/fzeA5s2bn4mPj69zaeajjz7Ko48+6nH/ggULnLc3btzo8XirVq1YtmwZuSVVnCyqoFsre3+0hx56iIceeqiGbOfOndmxY4fzb6VSDOD666/n+uuvd/69evVq5+3S0uoLlPHXXU/GJVdjsUpatWrFhg0bnI89++yzHMgpRa8TPP/88zz//PPO/l9KgGDW4//k/lmzPV7H+1+u9LjPnU6p5/H4v14CarZ2eejv/6Bz8n9IijGSW1LJyaJKuqSmOl/rlClTmDJlinOcue997LyY+de//sW//vUvr/Pdcsst3HKL+7VyzffGlZJKCw/9/R90a/VSjZY3kydPZvLkyR7yV199NVdffXWN+1x1nT17tvN+1+/BP//5T/75z386/96xY4fZ8dzCKVOmFCr3b9y40VkuOGPGjPwZM2Z4WNny8vItyu077rij4I477ijw+uJ8EJJfmZTya+xGw/W+N11uS+De2o4fKKcANePmga7YPeX9h2gUecWgBPJoFHnFoATSRzlDxX2nt6/xlWOPlRYOfsf3mpT3nVPwdYUfY9R5NIBU5PedLnGT928gDueVu8n7bi3vq0jAl7y3Kjtfe5JCRfPmzYuaN28euk6HIaCw3ERslD6kvbu8oYSMLDaJt3aHNikx6Kq/N8p3onp/Smj0kNJuTBSntbr1iv1/f9FOez+v0LdfKaowE23Qe72wOltpFL0o/Mb8veydULPgu8bxA+UU7DLqDFaS84wTC22auOvjW//iCrPHkcZ+x6+00Dy+OmTnLaegvN4qxxkqyuLiL6egeATuITtfC3JSjGengoALvpcch/smUdfXe7LIvcjBt4fSJNZIYbnJTb76vToXqDRbqTBbadvUe3VUKAnUfsW9MaNyUxGv7U75ux+qudlP6Q3m2XrF/j32N49BL6iyhNagmK02yqospCTGhLR/WqTTKExnoJg/eF/wfSXxwbsB8jd+bTwaBb8hJh/j63XCa6sMb/2zfOUUXMev+f74vmJvEms/9lg5ac8u79tAeDv2OND43jwOXyEpbyG+QPp492iC8lBsNput0a4C+WUmhBA0DXHvLm842694OXUU7IbDxUFxlntWN20MnS7eW68EnsOg87/bvzYUV5qR1KzuOltw/Da8fuCNxKD432cBNRdY/yEyz9LSQGW3nuP7zyl4G9+oFz5zCt7G9xcy8ja+75xCzTyO6/he5b0aRN8GwnnsscuiH2jBL3c79jjQ+MGFvLwn/YPcJb8jNze3SWM0KjabpKDcRJMYY4N0tVU8FF/H+dpsNT0UnXvIK2TNV5TmkPbbzrJhm+/W9Qp6R8fh2pQG+6KkwuI4L6lRLLGqcZyH0gTY4e3xRhHy8reAe9uroCaH4s2jcT+Eqoa86/hVFqINvnMKHvKOBdOfgXDXx9+CDJ4eTWB59Qu+ok9bYp3yvk47dJVXjHtJpZlkt9MUPeQrqk+pDKSPySNk598Dct+X5O28en9YLJapp06dmn/q1Kl6ObExLy+v3sIg5SYL+WVmUhKiKMup3/yJQm5hBWVRBvK8nPNxsqCC0hgDRY7PvdJs5UypiYoYeweH0ig9ZSbPI61rQ0mUnuJYI6eLKjGdMXI62kB+mcl+8VLouxtOaZWFwnIzuqKYkJSWSyk5WVRJXJSePYUNH2o9deqUwWq1JtfT8M4TG7092CgMSrGfEJNOJ0h0K6UtrrDnFBK85BSUvQruV+DxUXqvV3TePBpvZ6E45b3kdIr9LMi+xvedU/A0QP6qnryHyMw+cwreGjgWV5pp5yMe7+rRKBuN7Gez+M6JKPKKQQlUtaXoH2PUI6X02SZH0d/12ONKR8l3MLvkHUebem5PDxEZGRlS6YsVaq5/Yz0FZSZW/t+IBovdz3j+R/q2b8orN/etcX+l2crVj33LzCu7c++A8wFYf/AM097/lakXpTJ/3VFuHtSRJRuPhUSPCQPb8+jo8xnzjx+Y/YdeTOmXytSFmRwvrOCbP/X3+byvtp/k3uWb+e7B4XRvnehTTi2r9pzmj8syWXjnIHp2S6nzeMHSq1ev36SUnj1zGoBG4Y/5yymAZ9y8uNJi91y8XG3YOwIbvVzhe19wog16Yow6Dw8omJBRoCtqT/lgPZRg9fGXZA+FPupfr9LnTO34/vYkedPfX7j0bGPvqRKyDhdw86CODZoIbhEfRX5Zlcf9FQ7Pw/V3q4R9lR3koayAMuiE8yhisyMnUmm2Bmzbn5xgv7jKLfF8DbVh5e4c4qL0DE5V3z7lbKGRGBTfOQXwNChKu25fuO9t8LdgVsu7VoX5XjD1zoaM6sZPiLaH7NzH96V/bQ2QZ1I+kLwlOHmHPop3oHb8Mkefs0AelruBUKuPv3za2caC9YeINui4fmD7Bp23RUI0eaWerUsUo+G6oCtHbCvGJqQGRS+cp0iaHDm6cpPF64ZZV1ol2cNhp4rr3tldSsmqPTlc3DW53ku2I5FGZVB84Z6IDZSEdU/0qjEonknwYMb3rY+3Uyf96RNtsB97rNYj8Hbssb+cgvsC7uxz5idp7ipfGmDB9+VBqPWwAhmIYA3Q2cKZ0io+3Xyc6wa0b/AS6RbxUeSVeRqUCsfRzXEuoWcl71jueCy0HooOo07xUOwGpcJsC7iwt3Z0MXffoFsbdp0s5mRRJaN6tKrzWI2RRmJQfOcUwLO0NLABcvdQ/G988x4iC2Z8/2WrngbLd05BMUCKR+Pr7BQF5dhjZfxAOQVls2OwHoEiH6hVvO8FP0AOqFIZP1gD1LhPa1TLu78cxmSxMfXi1Aafu0VCFPllphql44Cz9NzVQ4nS62o85q2wpbbodQKdTmDQCadBqTRbiQ0Q8oox6mkSa+RUUd0NyqrdOQCM7NHwuZNIoFEYFKU1hy88cyj+DYS3nEugEJlnkt2ffE2PqbjCt4FwH99mk5T4ySko4ysGq8JsxWKTqvUPlFNQjj1WxvfX1gU8jz2uPpvF/4Jf7CYf0GCVq9PHp4Grp9YrkUCl2cq7Gw5zWc+WnJeS0ODzJydEY7VJCt3Ktcuq7EYj1kvIq9wZ8gpdWEgpYTbqdc4cSrnJQpyK0FPrpJiQeCgr9+SQ3qEpLRPr9aTdiCWiDYpyvRPIQAQfwjKozhHY5Y2qcxBOeYcHESin4D5+mfPsFHUGVE1Ix3V8NTkF1xxToPGVhozV4/v3CJQzVDxzHL5CZIrHZKkxvu+quXMvh/Lp5mPkl5mYenGXsMyvVOvluR3DUGH2DHk5k/KKQQnhPg2lDYxRLzA5dr5XmAJ7KACtmtTdoOSWVLHtWCGjerSs0ziNmYg2KMpmKX87qcF+tarsVbDLq/MIlI1MgfYpuBqsQDkFRR+1OQV3eTWHQSXVwkAEa4DU5izsuho8Fny172d1VwPv4xscxx6rNVjuxx6f7TkUk8XG6z8epF+HpmGrKkp25GzOuCXmFaMR7xryMihJefvnEhXCzZfKrv0og84l5BU4hwLQOim6zkn5H/fmICVcqhmUyKTaoAQ2EICzB1XAHEpM9V6FKkvgfQpJsUZKKu1nqAS7IKs5fjbJq0egzkCoOS/dXrTgvuCrM4hqxg/WAAWrv/fxvcsrxx67ju9rT9LZwMdZRzleWMGDl3UNW88op4fiVjqsGBR/Ia9Q5lBqhrxsWKw2TFZbwLJhsIe8ckuqfLaQUcOq3Tm0Toqhd9ukWo/R2Il4gxJonwLUjJtXmm1Y/OxTcJUvrjCrNhBg9zbULpjKGSqqQ1IVFocx9L9gKvLB7LOoabDUje/uAQVvEEM7vuvr9bcnCezteBRDVVJp9rknqbFTZbHy2qoDDOjYlBFh2ECnoOzjcC8dLq/yV+VVDwZFXzOHUuGIWAQqGwZ7yMsmPb0stVRZrKzdn8ulPVueU80g3Yl4g1JmsmALkFNw3T0eKMkLNQ2QshCql1cX0vHUx9+CacDkCKWp0V85ZEtK6aK/upyI2vcnOA/I6JHE96+/iwdR4bvPmdfxK80kRPvek+Shf4CCjsbMh5uOcqKokj9f3i2si1jTuCh0wl667Iq3fSgGnUAI17Lh+kjKC0xWm3Ovi5ocSus67kX59fd8ykzWczp/ApFuUKRUHQIC+4Kv5go8qYa8ipyFSymtmivqJt7G95MD8iYf6PXapL0HkTr9q489DpSzUHRVG2JS9HHKO/qc+Vso3ENYvvqceZcPfFhWzZyU702ijZmiCjMvrdjP4NTmXHR+fbVtUodeJ2geH+VxdV9hsiJEzb0mQgiMep2zfX1oy4btYxn1OswWW3AeimJQalk6vGpPDjFGHcPC/FmEmzp9mkKI5kKIH4QQ+x3/N/Mhly2E+E0IsVUIobqJkc11AVS5gBerWDBrs4C7j6/Wo1EbIvPUP9jxA8sXO8YPlFNoEmuk0myjymKlpNJCnI8+Z67ySpGDmuN2m8QanWXAgTaJKvKuITt/e5Jc9VHGPxs9lNd+PEBBuYnHxvSKiBBLi/hojyqvcpOV+ChPbzLa8V3SiWqvIhQoY0UZdJisNq/7YHyhbG485Xb2jhqklKzcc5ph552bu+NdqevlwSxgpZSyK7DS8bcvLpFS9gumaZnFpj6nAPa9CmpyCjVzKCrGj6sOYamN+VePr94ABTu+YhAD5hTcxg+UU3A9hExN63flDJUKs9VvI0xXeeUMFdUGyCUEp0YfpwGq59Maw8HhvDLe+fkQEwa0J61dk3CrA9g3N7rvli/3UbKreCUGvS5ga/lgqJlDqfZQYlQYlBbxUUQbdBwvDN6g7M8p5Wh+BZf2PLfDXVB3gzIOWOi4vRC4to7j1cCmsqpKeaxYZdLctf2HqjLXIOWbxHqGyFSN7zCIgXMKjtfrWPAD5RTcDVCgBdlT3v+C7P7+qBlfOfZYlXyMkTKTFYvVpk6fs9hDkVLy2LKdGPU6Zl7ZPdzqOElO8PRQ7N6k53uvVHoZdSIk7eIVXHMoZouszqGo8BqEEHRoHseR/PKAsu6sdOyOP5fLhRXqalBaSSlPAjj+9/WOSuB7IUSWEGK6vwGFENOFEJlCiMzS8gpVSW2jY6+C2qS5vdGkEgIKNiRll/d2HrtCkluIKUpFTsE5foW6nEK1Puqu2N3H90eS2+utr/GLVY9ffcFg7zoQWB/l2GO7PmePQVm29QRr9uUy88rutEyKnN3YLRI8cyi+jLnioeh1glCeAeaaQ3FNyqsJeQF0bB7HkfzgPZSVu0/Tu20SbZrU/5HLkU7AX5oQYgXQ2stDjwYxzzAp5QkhREvgByHEHinlGm+CUsq5wFyAll16yWIVISyoDouo8VB0zo7AFhD2qhNvV1IKcVF6DDpBcYWZKou9rt3o55fgPMPD4TEFOoujZkhKvYFQQnaqx6+wqEpqu+vTLM5/s0H3EFnbpv4XOvccVsCQV1zNnFGgJLt7yDGYs1AimbzSKp78Yif9OjRl0tDO4VanBskJ0ZRWWVQdhKYYFGOIQ15Kp+Eot5CXGg8F7AZl46H8GoezBSK/zMTmIwXcd2nX2il9lhHQoEgpL/P1mBDitBCijZTypBCiDZDjY4wTjv9zhBBLgUGAV4PiitUlh6JmEVEWEJ2ouTvXG03i7AZISVD7yyko7UWKKsyYrbaAC77SXkTxIAIZQ2W8IpUGomZZcnAeSkmVOWCfIfd9Oh2bxwXQx1V/S8CkueuhWWoWfNeQWqA9Sa7655RUBdyT1FiQUvLo0h2UVln41/V9QxoqCgUtHLvl88pMzsPYSiotzuopV5SLMX2IQ156t42NwZQNA3RoHkdplYWCct8H0Lmzem8ONsk5Xy6sUFeHczkw2XF7MrDMXUAIES+ESFRuA1fg4zxid5Qy10A5BaiOm6spQ4Xq0lK1Mfbq8QNfUUN1JZOa8Q16HQmO3d1q5F2PPVaXU3Bb8FXkLKrl1SXNgxlfkS8oD85AnCqqCLgnya6//fFjBfZ4+NkQ8np/4xG+3XmKmVd2D8mpgqEm2Us/r5JKi9djtYP1UNSGrJw5FEPwGxsB54VTMHmUlbtzSEmMpk+EFEeEm7oalDnA5UKI/cDljr8RQrQVQnztkGkFrBNCbAM2Al9JKb9VM7jVpQookIFoEhucgWjiZoACkVTP4wejv04nahjQQB6Q67HHwSz4ReXqPCxFPr+sigqzVXUI64SjokatPscKFHl1Bq5avnEblL2nSnjqi11c3DWZqReFpwFkIFokKP28XA2K9+9+dJAeilofpjqHYm8OWV02rO7zD9agmCw21uzLZVSPlmdlJ4baUKdfmpQyDxjl5f4TwGjH7d+B9NqMr3goahfwXZUWRwhI3QJ+IKeUaIMuKAOBlDQJkFNQ5JUcRGsVyVPljJNgDFCxSgPhGrJTM75yhkpuaRUmS+AQnzLecZULePWCb//hqs0BqTUQ7vKNOYdSUGZi2qJMEmOM/PvG9IhduBQPRUnM++uybTTYX4NBr86gqM2zGFxyKCaXHIraQ7w6NLeH6o7klamS35SdT0mVRavuciHid8oXqazSUUJYaqqG3OXV7KROcpwRouaKHao9mkBnoXgbX63+hSqT1Ip8bkklZqtUp0+swWkgAo2vHHt8TKW8coaKWgORFKQB8pBvpGehmK027l6cxaniSuZOGhjRZ2y0cOvn5a/LdpSzbFhdyEtt3l4JecUY9VSarVSYLMQYdaqNcFyUgdZJMfyeq86grNh9miiDjou6ntu7412JaIMCcLKwUtUC2CTWSGmVhcJykzr5uMgJYSnyBeUmSqvUVSUpJ8ypySmAfZENJgTUJEj5YMYXQpAUo94AxRj1RBl0tfZQGmNS3maTPPLJdjb8ns+c6/owoKPXJhQRQ1yUgbgovTOHUlLle4NuzbJhFR6KSoOgjBUbpafCZKXCbFUd7lLo1jqRvadLAspJKVmx+zQXntci6DnOZiLeoBwrKFe5ANplThRWqvIglL0KeaUm1QtsMB5Kk1gj+WUmVTkFRV5tTkGRD2bBDNZA2OUdSe0AVVse8sGOH+LXa9TriIvSN9ocin3z4g4+23Kch6/oxnUD2odbJVW0SIgiVzEofjwU58ZGvUCvwv1QG/JSPJ84ox6LTVJUYVGdkFfo1jKBAzmlzqMzfLHzRDFH8yu4qre3HRXnLhFvUOxxWHVJc0BV1ZCnvLoF02KTqnIKYA9hKUlBtfqUOeTVhqRKq9QfHqV4cGrHb+Kijzr9DUHq7zq+upBgtf7Bvd7G5KHYbJLZy3ey+Ncj3D3yPO695Pxwq6SaNkmxnHQ0Vyx09Gpr4sX7dPVQdCpWILVpI2XcOEdl2eniyqAvJrq1TqTKYguYmP/qt5PodYIrNINSg4g3KKB+wayWV7dABTN+UrDj15APVv/60CfI1xsT3PjB6l83eXU5I0DVnqRIocpi5f4PtrDwl8NMH96Fv1zZPSIaP6qlTdMYTjqaK+Y7+no1T/AsYFGS5AadTl2Vl8r3QPF8lDLj3JKqoA1K91b2kuy9p3yHvaSUfP3bSS48r4Xq/SrnCo3EoNT3ghZp44dfn7oZxPob36ATxKg4h1yRD9TnLFLIKank9rc28tX2k/xtdA/+Nrpno9DblTZNYu15PZsk33F6o7cFV1n4o406VSEvte+C00NxGBS7hxKcd9q1VQI6ATtPFPmU2XWymMN55Yzu0yaosc8FGkVwWW2Iwymvouqphnyw4we54KupMqqTfL3rU38GLtDZKe7ySbGB9yQpcq7/RzK//p7HfUu2UFJp5r839WNcv3bhVqlWtGkSg9kqOVNW5ew87K1tj5LriDHqVSXc1dpVZVwlb1JusnrdWOmPuCgDPdskkXW4wKfMF9vs4a4rtXCXB43EoIT+ijcpyAW5ZgioHsavEZIKTh9VZcxBhrBc9Vfzo1TGN+iE3z5n7vJqryAVfdSGMJT3M5LzJ+UmCy98t4931h+ic4t43v3jIHq0brznkbdxnClysrCS/DITTWKNXr8LiicRY9TXOSmvEzgP64p2eijBhXfdGdipGZ9kHcNitXmcA2Sx2vhs8zEu6Z6ihbu80EhCXo09hBX+nIKrvJqcgqu8mji3svvdf22M5/hKQz+18mqvOJsEaYAamq+2n+TKl9bw9s+HuH1IJ764/6JGbUwA2jp6eJ0sqiCvzOTs7+WOM+RlULdHxN/GQdfvpjKua++u2lxQDOzUjHKTlT1e8iir9+aSU1LFjRkdgh73XKCRGJTAXwrXk9IiL+lcv+MHk1MAdUlOb9U5auRtUp1Jqa282hJSRT4SE/IHckq59/3NxBkNfDB9CE+NSws6NBOJtG/m2GmeX87xggra+Og67azyEur2odw8qKOq+d1zKFC7C4pBqc0BWLv/jMdjSzYeITkhmku03fFeaSQGJbgvhZoQkOtZ1mrGdz3/pF5CcC5jqskpJAVpIILNJah5D2vKOzwUlS6KEpJSLa+Mr9IHiuR2Kxab5LkJffn6TxczpEuLcKsTMprGRZGcEM2+06UcKyinQzPvXaqV0JROJ1SXBPvCdbuI8pt2Nc7Bfo/BXlzQt30Tvt15qsb9+06XsHJPDrcO7qgqrHsu0ijeleAXwyDlVYzveiXl73AtBdcrY1U5hVp6BPUmHxf697DG+E4PJbjxbbb6Gb+2CCFuEELsFELYhBAZap7TvVUiN2Z0iLgW9KGgW6sEth8r5EypyemxuKO0SNHr1HucvnD1cJVxW7iUKqfUsl3NVWmt2Xa0kOwzZc77Xl65n1ijnikXdq6dsucAjcKgBOuhBBs3DTYsEsraeYWYIHf0Bu1BRJrBcsoHF/JSax+SgpSvAzuA61Bxvo9CI6sGDopurRLZd7oUgM7J8V5llM9EpyLkNeXCzn6NjquHq/zmXJPyLZOiVWjtyYSB7YnS63h99QEA1uzL5cvtJ5l2cSrNtGS8TxpF4DZYg6Imp+BKY6v3BzyqTwLh70RKbzSUQVEb8lK+A1LlE5Rkv1r52iKl3A2N8ztUHwzp0oIF67MB6NehqVcZpYOEUa9zehW+mD22N6ccu+9rg5pO395omRjD5As7MW/tIQSCr3ecpFurBO5pRJ0LwkGjMChqcgquaD9uT4Jtex4T5Huu9lQ8BddT+9SghDbUylusdvlAC1ZDIYSYDkwHiImJISNDVXTMg9zcXFJSUkKpWkjnlhLyc0qINui5doX3HEql2cbJnBKWfJ3Astl6Ei02bDbJgdxS2jaNJa+0CqtNEh9tIGPFE+Tm5pJvtRsGk9XmPJER7KXKAjBZJRkrnnDOYS6qpNxkYeyKhFq/Ximh4FQBLy2094YTzeO4aHHDBHXq+Dl3DaUuwdAoDIpG3Qn2Sr3SYr+KVGubiyvsvZtaJqoLMSgHMakNSeSWVAU1vtKkMBQt34UQKwBvu9gelVJ6nFLqDSnlXGAuQEZGhszMzKyVLhkZGdT2uXVF7dzKdy1UF3YZGRkcDuNrXr9hY40inoaat7afs+PAw7BQJ4MihLgBmA30BAZJKb2+A0KIq4D/AnpgvpRyTl3m1QieKov9ik7tBXuBo7lfK5ULco5jwfd2hrg3ThcHt+Ar8urHr3TI1y6G7oqU8rI6D3IOcbZFCBramDRm6vpOBUxICiH0wGvA1UAv4GYhRK86znvOU+k4jU5tSEdphaF2Qc5xLMhqPQinvEoPIqckuAVfkW+pVn+HgUupZQxdQ0MjeOpkUKSUu6WUewOIDQIOSCl/l1KagA+AcXWZtzGhdsG3OGLCapOISmv2Hm0S1ck7zqfo1kqdfIUjcXp+iroYtOIBdUnxXtnjjpLj6NxCnbxS/quc+x0I5V33VboaKoQQ44UQx4ChwFdCiO/qc77p06fX5/ARObf2mhsPIhRVMEKI1cDD3kJeQogJwFVSyqmOv28HBksp7/MxljN5CaRh94IijWTAcxtt+NH0Co7uUkp1FjaECCG+lVJe1dDzapwbhPP7FTCHEoKEpLdLdJ9WzDV5KYTIlFLWrhymHtH0Co5I1isc82rGRKM+Cef3K6BBCUFC8hjg2kmtPXCijmNqaGhoaEQYDVG+sAnoKoRIFUJEATcByxtgXg2NRo8QorkQ4gchxH7H/838yOqFEFuEEF821NxCiA5CiB+FELsdLWj+VIf5rhJC7BVCHBBCzPLyuBBCvOx4fLsQYkBt5wpy3lsd820XQqwXQqSHYl41c7vIXSCEsDpSCBFLnQyKr4SkEKKtEOJrACmlBbgP+A7YDXwkpdypcoq5ddGvHtH0Cg5Nr9ozC1gppewKrHT87Ys/Yf+NNeTcFuD/pJQ9gSHAvbWp4lRZDXo19k17XbHnWd8Idp5aznsIGCGl7Av8gxB9b9RWwDrk/oV9DY1spJTaP+2f9i9C/wF7gTaO222AvT7k2mNf9C8FvmzIud2eswy4vBZzDQW+c/n7r8Bf3WT+B9zsTb86vMaA87rJNwOOh+j9VTU38CBwL7AAmBDO72Ogf9qOHQ2NyKaVlPIkgON/XwdxvAT8BVDZjzmkcwMghOgM9Ad+rcVc7YCjLn8fc9wXrEx9zOvKH4Fv6jin6rmFEO2A8cCbIZqzXtFar2hohBl/lZQqnz8GyJFSZgkhRjbk3C7jJACfAg9KKYuDea4yhJf73KtBg6oYDeG8dkEhLsFuUC6q45zBzP0S8IiU0toYOhBEnIcihHheCLHHkQBbKoRo6vLYXx3Jq71CiCsbWC+f516EUy/H/KoSew2gx9tCiBwhxA6X+1QnletRL6+J40jQDeyVlFLKNC//lgGnhRBtHPq2AXK8DDEMGCuEyMa+cfhSIcR7DTQ3QggjdmOyWEr5WZAvX0FNNWh9VIyqGlMI0ReYD4yTUubVcc5g5s4APnB8thOA14UQ14Zo/tAT7pibl3jhFYDBcftfwL8ct3sB24BoIBU4COgbUK+eQHdgNZDhcn+49dI75uwCRDl06RWmz244MADY4XLfc8Asx+1ZyufZwHq1AQY4bicC+xyfW9h1U6H78246PhdAfiShy6EEnBv7VfYi4KU6zmUAfnf8hpTvcW83mWuwh5sE9gKAjSF4jWrm7QgcAC4M8WcbcG43+QVoOZTgkFJ+L+2VYQAbsFttsLdr+UBKWSWlPIT9Ax7UgHr5ajMTVr2IoNY2Uso1QL7b3eOAhY7bC4FrG1InsMf/pZSbHbdLsFdCtYsE3VQwB7hc2DvIXu74u0YlZZjnHgbcjt0r2ur4NzrYiaSPalAhxAwhxAyH2NfYF+ADwDzgnjq8tmDmfRxogd072CpCtCFW5dyNipC0XqkvhBBfAB9KKd8TQrwKbJBSvud47C3gGynlJw2s02pc2syEWy8RZGubBtCnM/Yr5DTH34VSyqYujxdIKcMSWnLM3xl7M9M04Egk6aah0dgJS1LeXyJQOtq5CCEexV7jvlh5mhf5kFpDNXp5e5qX+xrSSod7/kaDe+K4MSQ5NTQaE2ExKDJAOxchxGRgDDBKVrtQ9d7CJZBePgh3a5lwzx+I00KINlLKk/4Su/WNj8RxROimoXG2EHE5FGE/jOsRYKyUstzloeXATUKIaCFEKvbdshvDoaMb4dYr0lvbLAcmO25Pxr7xrUERdlfkLWC3lPI/kaSbhsbZRMTlUIQQB7BXTCmleRuklDMcjz0K3Ik9FPaglDJUG4zU6DUeeAVIAQqBrVLKK8Otl2P+0djr1fXA21LKpxtyfhc9lmCvMkoGTgNPAJ8DH2GvlDkC3CCldE/c17deFwFrgd+o3vj3N+wb8MKqm4bG2UTEGRQNDQ0NjcZJxIW8NDQ0NDQaJ5pB0dDQ0NAICZpB0dDQ0NAICZpB0dDQ0NAICZpB0dDQ0NAICZpB0dDQ0NAICZpB0dDQ0NAICf8PIvY1f4GIM3AAAAAASUVORK5CYII="
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## pandas + matplotlib\r\n",
    "\r\n",
    "任选第三章题目中一道题目，结合pandas和matplotlib给出分析结果。\r\n",
    "示例图：\r\n",
    "\r\n",
    "![在这里插入图片描述](https://img-blog.csdnimg.cn/df841ec22d9644458bf9a96c30d1e521.png)\r\n",
    "\r\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# 造数据\r\n",
    "import random\r\n",
    "f = open('data.txt', 'w')\r\n",
    "stu = ['Bernard', 'Kluicer', 'Anton', 'Falcon', 'Zima']\r\n",
    "\r\n",
    "\r\n",
    "def randTime():\r\n",
    "    t = str(random.randint(6, 19)).zfill(2)\r\n",
    "    m = str(random.randint(0, 59)).zfill(2)\r\n",
    "    s = str(random.randint(0, 59)).zfill(2)\r\n",
    "    return t+':'+m+':'+s\r\n",
    "\r\n",
    "\r\n",
    "print('ID\\tname\\tamount\\tpos\\tdate\\ttime', file=f)\r\n",
    "\r\n",
    "ordId = 0\r\n",
    "\r\n",
    "for day in range(1, 31):\r\n",
    "    for _ in range(random.randint(5*2, 5*3+3)):\r\n",
    "        ordId += 1\r\n",
    "        name = random.choice(stu)\r\n",
    "        amount = random.randint(60, 200)/10\r\n",
    "        pos = random.randint(1, 6)\r\n",
    "        tm = randTime()\r\n",
    "        print(str(ordId)+'\\t'+name+'\\t'+str(amount)+'\\t' +\r\n",
    "              str(pos)+'\\t'+'Sept '+str(day)+'\\t'+tm, file=f)\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "# 统计各位同学的消费金额\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import matplotlib\r\n",
    "matplotlib.rc(\"font\",family='DengXian',weight=\"bold\")\r\n",
    "df = pd.read_table('data.txt')\r\n",
    "df.groupby('name')['amount'].sum().sort_values().plot.bar()\r\n",
    "plt.legend()\r\n",
    "plt.title('各同学九月在食堂开销')\r\n",
    "plt.ylabel('金额（元）')\r\n",
    "plt.xlabel('姓名')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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"
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